





'''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    _lowerCamelCase  (__lowerCamelCase  :     Tuple     )   ->    List[Any]:
			a__     = 384
			if "tiny" in model_name:
						a__     = [3, 3, 9, 3]
						a__     = [96, 192, 384, 768]
			if "small" in model_name:
						a__     = [3, 3, 27, 3]
						a__     = [96, 192, 384, 768]
			if "base" in model_name:
						a__     = [3, 3, 27, 3]
						a__     = [128, 256, 512, 1024]
						a__     = 512
			if "large" in model_name:
						a__     = [3, 3, 27, 3]
						a__     = [192, 384, 768, 1536]
						a__     = 768
			if "xlarge" in model_name:
						a__     = [3, 3, 27, 3]
						a__     = [256, 512, 1024, 2048]
						a__     = 1024

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

			a__     = ConvNextConfig(
			    depths=__lowerCamelCase					,  hidden_sizes=__lowerCamelCase					,  out_features=["stage1", "stage2", "stage3", "stage4"]     )
			a__     = UperNetConfig(
			    backbone_config=__lowerCamelCase					,  auxiliary_in_channels=__lowerCamelCase					,  num_labels=__lowerCamelCase					,  idalabel=__lowerCamelCase					,  labelaid=__lowerCamelCase					,  )

			return config







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    Optional[Any]:
			a__     = []

			# 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    _lowerCamelCase  (__lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     Any     )   ->    List[Any]:
			a__     = dct.pop(__lowerCamelCase     )
			a__     = val







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     List[str]     )   ->    Union[str, Any]:
			a__     = {
			    "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__     = model_name_to_url[model_name]
			a__     = torch.hub.load_state_dict_from_url(__lowerCamelCase					,  map_location="cpu"     )["state_dict"]

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

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

			# rename keys
			a__     = create_rename_keys(__lowerCamelCase     )
			for src, dest in rename_keys:
						rename_key(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )

			model.load_state_dict(__lowerCamelCase     )

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

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

			with torch.no_grad():
						a__     = model(__lowerCamelCase     )

			if model_name == "upernet-convnext-tiny":
						a__     = torch.tensor(
						    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]     )
			elif model_name == "upernet-convnext-small":
						a__     = torch.tensor(
						    [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]     )
			elif model_name == "upernet-convnext-base":
						a__     = torch.tensor(
						    [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]     )
			elif model_name == "upernet-convnext-large":
						a__     = torch.tensor(
						    [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]     )
			elif model_name == "upernet-convnext-xlarge":
						a__     = torch.tensor(
						    [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]     )
			print("Logits:"					,  outputs.logits[0, 0, :3, :3]     )
			assert torch.allclose(outputs.logits[0, 0, :3, :3]					,  __lowerCamelCase					,  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(__lowerCamelCase     )
						print(f'''Saving processor to {pytorch_dump_folder_path}'''     )
						processor.save_pretrained(__lowerCamelCase     )

			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_   :   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."
		)

		lowerCAmelCase_   :   Tuple       =					parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''


import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script






@require_cpu
class       UpperCamelCase__  (		unittest.TestCase     ):







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				debug_launcher(test_script.main		)







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				debug_launcher(test_ops.main		)



