



"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      List[str]:
		lowercase__   :    int				=			SwinvaConfig()
		lowercase__   :    Optional[Any]				=			swinva_name.split('''_'''	)

		lowercase__   :    Union[str, Any]				=			name_split[1]
		if "to" in name_split[3]:
				lowercase__   :    Dict				=			int(name_split[3][-3:]	)
		else:
				lowercase__   :    str				=			int(name_split[3]	)
		if "to" in name_split[2]:
				lowercase__   :    str				=			int(name_split[2][-2:]	)
		else:
				lowercase__   :    Dict				=			int(name_split[2][6:]	)

		if model_size == "tiny":
				lowercase__   :    Optional[Any]				=			96
				lowercase__   :    Optional[int]				=			(2, 2, 6, 2)
				lowercase__   :    Union[str, Any]				=			(3, 6, 12, 24)
		elif model_size == "small":
				lowercase__   :    List[str]				=			96
				lowercase__   :    Any				=			(2, 2, 18, 2)
				lowercase__   :    List[Any]				=			(3, 6, 12, 24)
		elif model_size == "base":
				lowercase__   :    Optional[Any]				=			1_28
				lowercase__   :    Dict				=			(2, 2, 18, 2)
				lowercase__   :    List[Any]				=			(4, 8, 16, 32)
		else:
				lowercase__   :    Optional[Any]				=			1_92
				lowercase__   :    Optional[Any]				=			(2, 2, 18, 2)
				lowercase__   :    Any				=			(6, 12, 24, 48)

		if "to" in swinva_name:
				lowercase__   :    List[str]				=			(12, 12, 12, 6)

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				lowercase__   :    Tuple				=			2_18_41
				lowercase__   :    Any				=			'''huggingface/label-files'''
				lowercase__   :    str				=			'''imagenet-22k-id2label.json'''
				lowercase__   :    Union[str, Any]				=			json.load(open(hf_hub_download(__lowerCamelCase						,     __lowerCamelCase						,     repo_type='''dataset'''	)						,     '''r'''	)	)
				lowercase__   :    Tuple				=			{int(__lowerCamelCase	): v for k, v in idalabel.items()}
				lowercase__   :    Union[str, Any]				=			idalabel
				lowercase__   :    Tuple				=			{v: k for k, v in idalabel.items()}

		else:
				lowercase__   :    str				=			10_00
				lowercase__   :    Union[str, Any]				=			'''huggingface/label-files'''
				lowercase__   :    Dict				=			'''imagenet-1k-id2label.json'''
				lowercase__   :    Optional[int]				=			json.load(open(hf_hub_download(__lowerCamelCase						,     __lowerCamelCase						,     repo_type='''dataset'''	)						,     '''r'''	)	)
				lowercase__   :    Any				=			{int(__lowerCamelCase	): v for k, v in idalabel.items()}
				lowercase__   :    List[str]				=			idalabel
				lowercase__   :    Optional[Any]				=			{v: k for k, v in idalabel.items()}

		lowercase__   :    Tuple				=			img_size
		lowercase__   :    Dict				=			num_classes
		lowercase__   :    Union[str, Any]				=			embed_dim
		lowercase__   :    Optional[int]				=			depths
		lowercase__   :    Tuple				=			num_heads
		lowercase__   :    List[Any]				=			window_size

		return config



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Optional[Any]:
		if "patch_embed.proj" in name:
				lowercase__   :    List[str]				=			name.replace('''patch_embed.proj'''						,     '''embeddings.patch_embeddings.projection'''	)
		if "patch_embed.norm" in name:
				lowercase__   :    Optional[Any]				=			name.replace('''patch_embed.norm'''						,     '''embeddings.norm'''	)
		if "layers" in name:
				lowercase__   :    List[Any]				=			'''encoder.''' + name
		if "attn.proj" in name:
				lowercase__   :    Tuple				=			name.replace('''attn.proj'''						,     '''attention.output.dense'''	)
		if "attn" in name:
				lowercase__   :    Tuple				=			name.replace('''attn'''						,     '''attention.self'''	)
		if "norm1" in name:
				lowercase__   :    Optional[Any]				=			name.replace('''norm1'''						,     '''layernorm_before'''	)
		if "norm2" in name:
				lowercase__   :    Optional[int]				=			name.replace('''norm2'''						,     '''layernorm_after'''	)
		if "mlp.fc1" in name:
				lowercase__   :    Optional[int]				=			name.replace('''mlp.fc1'''						,     '''intermediate.dense'''	)
		if "mlp.fc2" in name:
				lowercase__   :    Union[str, Any]				=			name.replace('''mlp.fc2'''						,     '''output.dense'''	)
		if "q_bias" in name:
				lowercase__   :    Union[str, Any]				=			name.replace('''q_bias'''						,     '''query.bias'''	)
		if "k_bias" in name:
				lowercase__   :    str				=			name.replace('''k_bias'''						,     '''key.bias'''	)
		if "v_bias" in name:
				lowercase__   :    Dict				=			name.replace('''v_bias'''						,     '''value.bias'''	)
		if "cpb_mlp" in name:
				lowercase__   :    Any				=			name.replace('''cpb_mlp'''						,     '''continuous_position_bias_mlp'''	)
		if name == "norm.weight":
				lowercase__   :    Union[str, Any]				=			'''layernorm.weight'''
		if name == "norm.bias":
				lowercase__   :    int				=			'''layernorm.bias'''

		if "head" in name:
				lowercase__   :    Optional[Any]				=			name.replace('''head'''						,     '''classifier'''	)
		else:
				lowercase__   :    List[str]				=			'''swinv2.''' + name

		return name



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Union[str, Any]:
		for key in orig_state_dict.copy().keys():
				lowercase__   :    str				=			orig_state_dict.pop(__lowerCamelCase	)

				if "mask" in key:
						continue
				elif "qkv" in key:
						lowercase__   :    str				=			key.split('''.'''	)
						lowercase__   :    Tuple				=			int(key_split[1]	)
						lowercase__   :    int				=			int(key_split[3]	)
						lowercase__   :    Union[str, Any]				=			model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								lowercase__   :    Tuple				=			val[:dim, :]
								lowercase__   :    Any				=			val[dim : dim * 2, :]
								lowercase__   :    str				=			val[-dim:, :]
						else:
								lowercase__   :    str				=			val[:dim]
								lowercase__   :    int				=			val[
								    dim : dim * 2
								]
								lowercase__   :    Optional[int]				=			val[-dim:]
				else:
						lowercase__   :    int				=			val

		return orig_state_dict



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    Dict				=			timm.create_model(__lowerCamelCase						,     pretrained=__lowerCamelCase	)
		timm_model.eval()

		lowercase__   :    Union[str, Any]				=			get_swinva_config(__lowerCamelCase	)
		lowercase__   :    Tuple				=			SwinvaForImageClassification(__lowerCamelCase	)
		model.eval()

		lowercase__   :    str				=			convert_state_dict(timm_model.state_dict()						,     __lowerCamelCase	)
		model.load_state_dict(__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''

		lowercase__   :    Any				=			AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''						,     '''-'''	)	)	)
		lowercase__   :    Optional[Any]				=			Image.open(requests.get(__lowerCamelCase						,     stream=__lowerCamelCase	).raw	)
		lowercase__   :    Tuple				=			image_processor(images=__lowerCamelCase						,     return_tensors='''pt'''	)

		lowercase__   :    str				=			timm_model(inputs['''pixel_values''']	)
		lowercase__   :    List[str]				=			model(**__lowerCamelCase	).logits

		assert torch.allclose(__lowerCamelCase						,     __lowerCamelCase						,     atol=1E-3	)

		print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""	)
		model.save_pretrained(__lowerCamelCase	)

		print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
		image_processor.save_pretrained(__lowerCamelCase	)

		model.push_to_hub(
		    repo_path_or_name=Path(__lowerCamelCase						,     __lowerCamelCase	)						,     organization='''nandwalritik'''						,     commit_message='''Add model'''						,     )


if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--swinv2_name',
							    default='swinv2_tiny_patch4_window8_256',
							    type=str,
							    help='Name of the Swinv2 timm 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.'
							)

							lowerCAmelCase_   						=   parser.parse_args()
							convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)



import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any      )							->      int:





    '''simple docstring'''






    A__          =   384
    A__          =   7
    if "tiny" in model_name:
        A__          =   96
        A__          =   (2, 2, 6, 2)
        A__          =   (3, 6, 12, 24)
    elif "small" in model_name:
        A__          =   96
        A__          =   (2, 2, 18, 2)
        A__          =   (3, 6, 12, 24)
    elif "base" in model_name:
        A__          =   128
        A__          =   (2, 2, 18, 2)
        A__          =   (4, 8, 16, 32)
        A__          =   12
        A__          =   512
    elif "large" in model_name:
        A__          =   192
        A__          =   (2, 2, 18, 2)
        A__          =   (6, 12, 24, 48)
        A__          =   12
        A__          =   768

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

    A__          =   SwinConfig(
        embed_dim=SCREAMING_SNAKE_CASE__    ,			depths=SCREAMING_SNAKE_CASE__    ,			num_heads=SCREAMING_SNAKE_CASE__    ,			window_size=SCREAMING_SNAKE_CASE__    ,			out_features=['stage1', 'stage2', 'stage3', 'stage4']    ,			)
    A__          =   UperNetConfig(
        backbone_config=SCREAMING_SNAKE_CASE__    ,			auxiliary_in_channels=SCREAMING_SNAKE_CASE__    ,			num_labels=SCREAMING_SNAKE_CASE__    ,			idalabel=SCREAMING_SNAKE_CASE__    ,			labelaid=SCREAMING_SNAKE_CASE__    ,			)

    return config
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Dict:





    '''simple docstring'''






    A__          =   []

    # fmt: off
    # stem
    rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')      )
    rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')      )
    rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')      )
    rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.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}.blocks.{j}.norm1.weight', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm1.bias', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table', f'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index', f'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight', f'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias', f'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm2.weight', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm2.bias', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight', f'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias', f'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight', f'backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight')      )
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias', f'backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias')      )

        if i < 3:
            rename_keys.append((f'backbone.stages.{i}.downsample.reduction.weight', f'backbone.encoder.layers.{i}.downsample.reduction.weight')      )
            rename_keys.append((f'backbone.stages.{i}.downsample.norm.weight', f'backbone.encoder.layers.{i}.downsample.norm.weight')      )
            rename_keys.append((f'backbone.stages.{i}.downsample.norm.bias', f'backbone.encoder.layers.{i}.downsample.norm.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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   dct.pop(SCREAMING_SNAKE_CASE__      )
    A__          =   val
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      Any:





    '''simple docstring'''






    A__          =   [int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
    for i in range(len(backbone_config.depths      )      ):
        A__          =   num_features[i]
        for j in range(backbone_config.depths[i]      ):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            A__          =   state_dict.pop(f'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'      )
            A__          =   state_dict.pop(f'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'      )
            # next, add query, keys and values (in that order) to the state dict
            A__          =   in_proj_weight[:dim, :]
            A__          =   in_proj_bias[: dim]
            A__          =   in_proj_weight[
                dim : dim * 2, :
            ]
            A__          =   in_proj_bias[
                dim : dim * 2
            ]
            A__          =   in_proj_weight[
                -dim :, :
            ]
            A__          =   in_proj_bias[-dim :]
            # fmt: on
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Optional[Any]:





    '''simple docstring'''






    A__      ,						A__          =   x.shape
    A__          =   x.reshape(SCREAMING_SNAKE_CASE__    ,			4    ,			in_channel // 4      )
    A__          =   x[:, [0, 2, 1, 3], :].transpose(1    ,			2      ).reshape(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple      )							->      List[str]:





    '''simple docstring'''






    A__      ,						A__          =   x.shape
    A__          =   x.reshape(SCREAMING_SNAKE_CASE__    ,			in_channel // 4    ,			4      )
    A__          =   x[:, :, [0, 2, 1, 3]].transpose(1    ,			2      ).reshape(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   x.shape[0]
    A__          =   x.reshape(4    ,			in_channel // 4      )
    A__          =   x[[0, 2, 1, 3], :].transpose(0    ,			1      ).reshape(SCREAMING_SNAKE_CASE__      )
    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any      )							->      List[Any]:





    '''simple docstring'''






    A__          =   x.shape[0]
    A__          =   x.reshape(in_channel // 4    ,			4      )
    A__          =   x[:, [0, 2, 1, 3]].transpose(0    ,			1      ).reshape(SCREAMING_SNAKE_CASE__      )
    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Union[str, Any]:





    '''simple docstring'''






    A__          =   {
        'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
        'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
        'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
        'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
    }
    A__          =   model_name_to_url[model_name]
    A__          =   torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__    ,			map_location='cpu'    ,			file_name=SCREAMING_SNAKE_CASE__      )[
        'state_dict'
    ]

    for name, param in state_dict.items():
        print(SCREAMING_SNAKE_CASE__    ,			param.shape      )

    A__          =   get_upernet_config(SCREAMING_SNAKE_CASE__      )
    A__          =   UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE__      )
    model.eval()

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

    # rename keys
    A__          =   create_rename_keys(SCREAMING_SNAKE_CASE__      )
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    read_in_q_k_v(SCREAMING_SNAKE_CASE__    ,			config.backbone_config      )

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                A__          =   reverse_correct_unfold_reduction_order(SCREAMING_SNAKE_CASE__      )
            if "norm" in key:
                A__          =   reverse_correct_unfold_norm_order(SCREAMING_SNAKE_CASE__      )

    model.load_state_dict(SCREAMING_SNAKE_CASE__      )

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

    A__          =   SegformerImageProcessor()
    A__          =   processor(SCREAMING_SNAKE_CASE__    ,			return_tensors='pt'      ).pixel_values

    with torch.no_grad():
        A__          =   model(SCREAMING_SNAKE_CASE__      )
        A__          =   outputs.logits

    print(logits.shape      )
    print('First values of logits:'    ,			logits[0, 0, :3, :3]      )
    # assert values
    if model_name == "upernet-swin-tiny":
        A__          =   torch.tensor(
            [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]      )
    elif model_name == "upernet-swin-small":
        A__          =   torch.tensor(
            [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]      )
    elif model_name == "upernet-swin-base":
        A__          =   torch.tensor(
            [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]      )
    elif model_name == "upernet-swin-large":
        A__          =   torch.tensor(
            [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]      )
    print('Logits:'    ,			outputs.logits[0, 0, :3, :3]      )
    assert torch.allclose(outputs.logits[0, 0, :3, :3]    ,			SCREAMING_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(SCREAMING_SNAKE_CASE__      )
        print(f'Saving processor to {pytorch_dump_folder_path}'      )
        processor.save_pretrained(SCREAMING_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__":
     lowercase_       		=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--model_name",
         default="upernet-swin-tiny",
         type=str,
         choices=[f"""upernet-swin-{size}""" for size in ["tiny", "small", "base", "large"]],
         help="Name of the Swin + 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."
     )

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

