


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   (      __magic_name__	:				List[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						384
		if "tiny" in model_name:
				lowercase__	     =						[3, 3, 9, 3]
				lowercase__	     =						[96, 192, 384, 768]
		if "small" in model_name:
				lowercase__	     =						[3, 3, 27, 3]
				lowercase__	     =						[96, 192, 384, 768]
		if "base" in model_name:
				lowercase__	     =						[3, 3, 27, 3]
				lowercase__	     =						[128, 256, 512, 1024]
				lowercase__	     =						512
		if "large" in model_name:
				lowercase__	     =						[3, 3, 27, 3]
				lowercase__	     =						[192, 384, 768, 1536]
				lowercase__	     =						768
		if "xlarge" in model_name:
				lowercase__	     =						[3, 3, 27, 3]
				lowercase__	     =						[256, 512, 1024, 2048]
				lowercase__	     =						1024

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

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

		return config






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						[]

		# 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   (      __magic_name__	:				Optional[int]       ,       __magic_name__	:				str       ,       __magic_name__	:				List[str]	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__	     =						dct.pop(__magic_name__	)
		lowercase__	     =						val






def 					UpperCamelCase   (      __magic_name__	:				Optional[int]       ,       __magic_name__	:				Optional[Any]       ,       __magic_name__	:				Union[str, Any]	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						{
		    """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""",
		}
		lowercase__	     =						model_name_to_url[model_name]
		lowercase__	     =						torch.hub.load_state_dict_from_url(__magic_name__       ,       map_location="""cpu"""	)["""state_dict"""]

		lowercase__	     =						get_upernet_config(__magic_name__	)
		lowercase__	     =						UperNetForSemanticSegmentation(__magic_name__	)
		model.eval()

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

		# rename keys
		lowercase__	     =						create_rename_keys(__magic_name__	)
		for src, dest in rename_keys:
				rename_key(__magic_name__       ,       __magic_name__       ,       __magic_name__	)

		model.load_state_dict(__magic_name__	)

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

		lowercase__	     =						SegformerImageProcessor()
		lowercase__	     =						processor(__magic_name__       ,       return_tensors="""pt"""	).pixel_values

		with torch.no_grad():
				lowercase__	     =						model(__magic_name__	)

		if model_name == "upernet-convnext-tiny":
				lowercase__	     =						torch.tensor(
				    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]	)
		elif model_name == "upernet-convnext-small":
				lowercase__	     =						torch.tensor(
				    [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]	)
		elif model_name == "upernet-convnext-base":
				lowercase__	     =						torch.tensor(
				    [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]	)
		elif model_name == "upernet-convnext-large":
				lowercase__	     =						torch.tensor(
				    [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]	)
		elif model_name == "upernet-convnext-xlarge":
				lowercase__	     =						torch.tensor(
				    [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]	)
		print("""Logits:"""       ,       outputs.logits[0, 0, :3, :3]	)
		assert torch.allclose(outputs.logits[0, 0, :3, :3]       ,       __magic_name__       ,       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(__magic_name__	)
				print(f'''Saving processor to {pytorch_dump_folder_path}'''	)
				processor.save_pretrained(__magic_name__	)

		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			:						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.'
	)

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




from __future__ import annotations
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]			,a__    :   Dict			,a__    :   Union[str, Any]			,a__    :   Any       )					->				Optional[int]:  # noqa: E741
	while r - l > 1:
		__A :			Any		    =			(l + r) // 2
		if v[m] >= key:
			__A :			Optional[int]		    =			m
		else:
			__A :			List[Any]		    =			m  # noqa: E741
	return r
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[int]       )					->				int:
	if len(a__       ) == 0:
		return 0

	__A :			str		    =			[0] * len(a__       )
	__A :			List[str]		    =			1

	__A :			List[Any]		    =			v[0]

	for i in range(1			,len(a__       )       ):
		if v[i] < tail[0]:
			__A :			int		    =			v[i]
		elif v[i] > tail[length - 1]:
			__A :			Union[str, Any]		    =			v[i]
			length += 1
		else:
			__A :			Any		    =			v[i]

	return length


if __name__ == "__main__":
			import doctest

			doctest.testmod()





