






"""simple docstring"""






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       _A    (__a							)				-> Any:
				"""simple docstring"""






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

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

				SCREAMING_SNAKE_CASE_   :						str							=	ConvNextConfig(
				    depths=__a					,       hidden_sizes=__a					,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							)
				SCREAMING_SNAKE_CASE_   :						int							=	UperNetConfig(
				    backbone_config=__a					,       auxiliary_in_channels=__a					,       num_labels=__a					,       idalabel=__a					,       labelaid=__a					,       )

				return config



def       _A    (__a							)				-> List[Any]:
				"""simple docstring"""






				SCREAMING_SNAKE_CASE_   :						Optional[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       _A    (__a					,       __a					,       __a							)				-> List[Any]:
				"""simple docstring"""






				SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	dct.pop(__a							)
				SCREAMING_SNAKE_CASE_   :						List[str]							=	val



def       _A    (__a					,       __a					,       __a							)				-> Any:
				"""simple docstring"""






				SCREAMING_SNAKE_CASE_   :						Dict							=	{
				    '''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''',
				}
				SCREAMING_SNAKE_CASE_   :						str							=	model_name_to_url[model_name]
				SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.hub.load_state_dict_from_url(__a					,       map_location='''cpu'''							)['''state_dict''']

				SCREAMING_SNAKE_CASE_   :						str							=	get_upernet_config(__a							)
				SCREAMING_SNAKE_CASE_   :						Any							=	UperNetForSemanticSegmentation(__a							)
				model.eval()

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

				# rename keys
				SCREAMING_SNAKE_CASE_   :						str							=	create_rename_keys(__a							)
				for src, dest in rename_keys:
								rename_key(__a					,       __a					,       __a							)

				model.load_state_dict(__a							)

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

				SCREAMING_SNAKE_CASE_   :						Dict							=	SegformerImageProcessor()
				SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	processor(__a					,       return_tensors='''pt'''							).pixel_values

				with torch.no_grad():
								SCREAMING_SNAKE_CASE_   :						Optional[int]							=	model(__a							)

				if model_name == "upernet-convnext-tiny":
								SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	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":
								SCREAMING_SNAKE_CASE_   :						str							=	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":
								SCREAMING_SNAKE_CASE_   :						List[Any]							=	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":
								SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	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":
								SCREAMING_SNAKE_CASE_   :						List[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]					,       __a					,       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(__a							)
								print(f'Saving processor to {pytorch_dump_folder_path}'							)
								processor.save_pretrained(__a							)

				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_     :				Union[str, Any]         =				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_     :				Any         =				parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""






from __future__ import annotations



def       _A    (__a					,       __a					,       __a							)				-> int | float:
    """simple docstring"""






    if len(__a							) == 0:
        raise ValueError('''find_max() arg is an empty sequence'''							)
    if (
        left >= len(__a							)
        or left < -len(__a							)
        or right >= len(__a							)
        or right < -len(__a							)
    ):
        raise IndexError('''list index out of range'''							)
    if left == right:
        return nums[left]
    SCREAMING_SNAKE_CASE_   :						str							=	(left + right) >> 1  # the middle
    SCREAMING_SNAKE_CASE_   :						int							=	find_max(__a					,       __a					,       __a							)  # find max in range[left, mid]
    SCREAMING_SNAKE_CASE_   :						str							=	find_max(__a					,       mid + 1					,       __a							)  # find max in range[mid + 1, right]

    return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)


