



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_		:		List[Any]):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					384
				if "tiny" in model_name:
								lowerCAmelCase__					:    List[str]												=					[3, 3, 9, 3]
								lowerCAmelCase__					:    List[Any]												=					[96, 192, 384, 768]
				if "small" in model_name:
								lowerCAmelCase__					:    Optional[int]												=					[3, 3, 27, 3]
								lowerCAmelCase__					:    Tuple												=					[96, 192, 384, 768]
				if "base" in model_name:
								lowerCAmelCase__					:    Any												=					[3, 3, 27, 3]
								lowerCAmelCase__					:    Union[str, Any]												=					[128, 256, 512, 1024]
								lowerCAmelCase__					:    Dict												=					512
				if "large" in model_name:
								lowerCAmelCase__					:    Tuple												=					[3, 3, 27, 3]
								lowerCAmelCase__					:    Optional[int]												=					[192, 384, 768, 1536]
								lowerCAmelCase__					:    Optional[Any]												=					768
				if "xlarge" in model_name:
								lowerCAmelCase__					:    Any												=					[3, 3, 27, 3]
								lowerCAmelCase__					:    int												=					[256, 512, 1024, 2048]
								lowerCAmelCase__					:    List[Any]												=					1024

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

				lowerCAmelCase__					:    Optional[Any]												=					ConvNextConfig(
				    depths=snake_case__	,hidden_sizes=snake_case__	,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''])
				lowerCAmelCase__					:    List[Any]												=					UperNetConfig(
				    backbone_config=snake_case__	,auxiliary_in_channels=snake_case__	,num_labels=snake_case__	,idalabel=snake_case__	,labelaid=snake_case__	,)

				return config






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]):





				'''simple docstring'''

				lowerCAmelCase__					:    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      lowerCAmelCase__		(   lowerCamelCase_		:		Dict	,lowerCamelCase_		:		str	,lowerCamelCase_		:		Dict):





				'''simple docstring'''

				lowerCAmelCase__					:    List[str]												=					dct.pop(snake_case__)
				lowerCAmelCase__					:    Optional[Any]												=					val






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[Any]	,lowerCamelCase_		:		Tuple	,lowerCamelCase_		:		Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					{
				    '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',
				}
				lowerCAmelCase__					:    Optional[Any]												=					model_name_to_url[model_name]
				lowerCAmelCase__					:    Dict												=					torch.hub.load_state_dict_from_url(snake_case__	,map_location='''cpu''')['state_dict']

				lowerCAmelCase__					:    List[str]												=					get_upernet_config(snake_case__)
				lowerCAmelCase__					:    int												=					UperNetForSemanticSegmentation(snake_case__)
				model.eval()

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

				# rename keys
				lowerCAmelCase__					:    Optional[Any]												=					create_rename_keys(snake_case__)
				for src, dest in rename_keys:
								rename_key(snake_case__	,snake_case__	,snake_case__)

				model.load_state_dict(snake_case__)

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

				lowerCAmelCase__					:    Tuple												=					SegformerImageProcessor()
				lowerCAmelCase__					:    Any												=					processor(snake_case__	,return_tensors='''pt''').pixel_values

				with torch.no_grad():
								lowerCAmelCase__					:    str												=					model(snake_case__)

				if model_name == "upernet-convnext-tiny":
								lowerCAmelCase__					:    List[str]												=					torch.tensor(
								    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]])
				elif model_name == "upernet-convnext-small":
								lowerCAmelCase__					:    List[Any]												=					torch.tensor(
								    [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]])
				elif model_name == "upernet-convnext-base":
								lowerCAmelCase__					:    Tuple												=					torch.tensor(
								    [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]])
				elif model_name == "upernet-convnext-large":
								lowerCAmelCase__					:    Optional[int]												=					torch.tensor(
								    [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]])
				elif model_name == "upernet-convnext-xlarge":
								lowerCAmelCase__					:    List[Any]												=					torch.tensor(
								    [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]])
				print('''Logits:'''	,outputs.logits[0, 0, :3, :3])
				assert torch.allclose(outputs.logits[0, 0, :3, :3]	,snake_case__	,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(snake_case__)
								print(f"""Saving processor to {pytorch_dump_folder_path}""")
								processor.save_pretrained(snake_case__)

				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__":
							__snake_case    :		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.'
							)

							__snake_case    :		List[str]  =parser.parse_args()
							convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






__snake_case    :		Any  ='\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

__snake_case    :		Tuple  =[{'type': 'code', 'content': INSTALL_CONTENT}]
__snake_case    :		Tuple  ={
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

