






import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Union[str, Any]    )						->      List[Any]:





    """simple docstring"""

    __A         =	fname.split(os.path.sep    )[-1]
    return re.search(R"""^(.*)_\d+\.jpg$"""			,				__lowercase    ).groups()[0]


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''




     def __init__(				self       :			List[str] ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			List[Any]=None ,	UpperCamelCase_       :			List[str]=None				):


         """simple docstring"""



         __A         =	file_names
         __A         =	image_transform
         __A         =	label_to_id




     def __len__(				self       :			List[Any]				):


         """simple docstring"""



         return len(self.file_names				)





     def __getitem__(				self       :			Optional[int] ,	UpperCamelCase_       :			List[Any]				):


         """simple docstring"""



         __A         =	self.file_names[idx]
         __A         =	PIL.Image.open(UpperCamelCase_				)
         __A         =	raw_image.convert("""RGB"""				)
         if self.image_transform is not None:
             __A         =	self.image_transform(UpperCamelCase_				)
         __A         =	extract_label(UpperCamelCase_				)
         if self.label_to_id is not None:
             __A         =	self.label_to_id[label]
         return {"image": image, "label": label}



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Union[str, Any]			,				__lowercase   :			Any    )						->      Optional[Any]:





    """simple docstring"""

    if args.with_tracking:
        __A         =	Accelerator(
            cpu=args.cpu			,				mixed_precision=args.mixed_precision			,				log_with="""all"""			,				project_dir=args.project_dir    )
    else:
        __A         =	Accelerator(cpu=args.cpu			,				mixed_precision=args.mixed_precision    )

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __A         =	config["""lr"""]
    __A         =	int(config["""num_epochs"""]    )
    __A         =	int(config["""seed"""]    )
    __A         =	int(config["""batch_size"""]    )
    __A         =	config["""image_size"""]
    if not isinstance(__lowercase			,				(list, tuple)    ):
        __A         =	(image_size, image_size)

    # Parse out whether we are saving every epoch or after a certain number of batches
    if hasattr(args.checkpointing_steps			,				"""isdigit"""    ):
        if args.checkpointing_steps == "epoch":
            __A         =	args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            __A         =	int(args.checkpointing_steps    )
        else:
            raise ValueError(
                f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."    )
    else:
        __A         =	None

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        __A         =	os.path.split(__lowercase    )[-1].split("""."""    )[0]
        accelerator.init_trackers(__lowercase			,				__lowercase    )

    # Grab all the image filenames
    __A         =	[os.path.join(args.data_dir			,				__lowercase    ) for fname in os.listdir(args.data_dir    ) if fname.endswith(""".jpg"""    )]

    # Build the label correspondences
    __A         =	[extract_label(__lowercase    ) for fname in file_names]
    __A         =	list(set(__lowercase    )    )
    id_to_label.sort()
    __A         =	{lbl: i for i, lbl in enumerate(__lowercase    )}

    # Set the seed before splitting the data.
    np.random.seed(__lowercase    )
    torch.manual_seed(__lowercase    )
    torch.cuda.manual_seed_all(__lowercase    )

    # Split our filenames between train and validation
    __A         =	np.random.permutation(len(__lowercase    )    )
    __A         =	int(0.8 * len(__lowercase    )    )
    __A         =	random_perm[:cut]
    __A         =	random_perm[cut:]

    # For training we use a simple RandomResizedCrop
    __A         =	Compose([RandomResizedCrop(__lowercase			,				scale=(0.5, 1.0)    ), ToTensor()]    )
    __A         =	PetsDataset(
        [file_names[i] for i in train_split]			,				image_transform=__lowercase			,				label_to_id=__lowercase    )

    # For evaluation, we use a deterministic Resize
    __A         =	Compose([Resize(__lowercase    ), ToTensor()]    )
    __A         =	PetsDataset([file_names[i] for i in eval_split]			,				image_transform=__lowercase			,				label_to_id=__lowercase    )

    # Instantiate dataloaders.
    __A         =	DataLoader(__lowercase			,				shuffle=__lowercase			,				batch_size=__lowercase			,				num_workers=4    )
    __A         =	DataLoader(__lowercase			,				shuffle=__lowercase			,				batch_size=__lowercase			,				num_workers=4    )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __A         =	create_model("""resnet50d"""			,				pretrained=__lowercase			,				num_classes=len(__lowercase    )    )

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    __A         =	model.to(accelerator.device    )

    # Freezing the base model
    for param in model.parameters():
        __A         =	False
    for param in model.get_classifier().parameters():
        __A         =	True

    # We normalize the batches of images to be a bit faster.
    __A         =	torch.tensor(model.default_cfg["""mean"""]    )[None, :, None, None].to(accelerator.device    )
    __A         =	torch.tensor(model.default_cfg["""std"""]    )[None, :, None, None].to(accelerator.device    )

    # Instantiate optimizer
    __A         =	torch.optim.Adam(params=model.parameters()			,				lr=lr / 2_5    )

    # Instantiate learning rate scheduler
    __A         =	OneCycleLR(optimizer=__lowercase			,				max_lr=__lowercase			,				epochs=__lowercase			,				steps_per_epoch=len(__lowercase    )    )

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    __A    ,	__A    ,	__A    ,	__A    ,	__A         =	accelerator.prepare(
        __lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase    )
    # We need to keep track of how many total steps we have iterated over
    __A         =	0
    # We also need to keep track of the starting epoch so files are named properly
    __A         =	0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"    )
            accelerator.load_state(args.resume_from_checkpoint    )
            __A         =	os.path.basename(args.resume_from_checkpoint    )
        else:
            # Get the most recent checkpoint
            __A         =	[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
            dirs.sort(key=os.path.getctime    )
            __A         =	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        __A         =	os.path.splitext(__lowercase    )[0]

        if "epoch" in training_difference:
            __A         =	int(training_difference.replace("""epoch_"""			,				""""""    )    ) + 1
            __A         =	None
        else:
            __A         =	int(training_difference.replace("""step_"""			,				""""""    )    )
            __A         =	resume_step // len(__lowercase    )
            resume_step -= starting_epoch * len(__lowercase    )

    # Now we train the model
    for epoch in range(__lowercase			,				__lowercase    ):
        model.train()
        if args.with_tracking:
            __A         =	0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            __A         =	accelerator.skip_first_batches(__lowercase			,				__lowercase    )
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            __A         =	train_dataloader
        for batch in active_dataloader:
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            __A         =	{k: v.to(accelerator.device    ) for k, v in batch.items()}
            __A         =	(batch["""image"""] - mean) / std
            __A         =	model(__lowercase    )
            __A         =	torch.nn.functional.cross_entropy(__lowercase			,				batch["""label"""]    )
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(__lowercase    )
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            overall_step += 1
            if isinstance(__lowercase			,				__lowercase    ):
                __A         =	f"step_{overall_step}"
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        __A         =	os.path.join(args.output_dir			,				__lowercase    )
                    accelerator.save_state(__lowercase    )
        model.eval()
        __A         =	0
        __A         =	0
        for step, batch in enumerate(__lowercase    ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            __A         =	{k: v.to(accelerator.device    ) for k, v in batch.items()}
            __A         =	(batch["""image"""] - mean) / std
            with torch.no_grad():
                __A         =	model(__lowercase    )
            __A         =	outputs.argmax(dim=-1    )
            __A    ,	__A         =	accelerator.gather_for_metrics((predictions, batch["""label"""])    )
            __A         =	predictions == references
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        __A         =	accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"epoch {epoch}: {1_0_0 * eval_metric:.2f}"    )
        if args.with_tracking:
            accelerator.log(
                {
                    """accuracy""": 1_0_0 * eval_metric,
                    """train_loss""": total_loss.item() / len(__lowercase    ),
                    """epoch""": epoch,
                }			,				step=__lowercase			,				)
        if checkpointing_steps == "epoch":
            __A         =	f"epoch_{epoch}"
            if args.output_dir is not None:
                __A         =	os.path.join(args.output_dir			,				__lowercase    )
            accelerator.save_state(__lowercase    )

    if args.with_tracking:
        accelerator.end_training()



def 			_SCREAMING_SNAKE_CASE						( )						->      Dict:





    """simple docstring"""

    __A         =	argparse.ArgumentParser(description="""Simple example of training script."""    )
    parser.add_argument("""--data_dir"""			,				required=__lowercase			,				help="""The data folder on disk."""    )
    parser.add_argument("""--fp16"""			,				action="""store_true"""			,				help="""If passed, will use FP16 training."""    )
    parser.add_argument(
        """--mixed_precision"""			,				type=__lowercase			,				default=__lowercase			,				choices=["""no""", """fp16""", """bf16""", """fp8"""]			,				help="""Whether to use mixed precision. Choose"""
        """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
        """and an Nvidia Ampere GPU."""			,				)
    parser.add_argument("""--cpu"""			,				action="""store_true"""			,				help="""If passed, will train on the CPU."""    )
    parser.add_argument(
        """--checkpointing_steps"""			,				type=__lowercase			,				default=__lowercase			,				help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""			,				)
    parser.add_argument(
        """--output_dir"""			,				type=__lowercase			,				default="""."""			,				help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,				)
    parser.add_argument(
        """--resume_from_checkpoint"""			,				type=__lowercase			,				default=__lowercase			,				help="""If the training should continue from a checkpoint folder."""			,				)
    parser.add_argument(
        """--with_tracking"""			,				action="""store_true"""			,				help="""Whether to load in all available experiment trackers from the environment and use them for logging."""			,				)
    parser.add_argument(
        """--project_dir"""			,				type=__lowercase			,				default="""logs"""			,				help="""Location on where to store experiment tracking logs` and relevent project information"""			,				)
    __A         =	parser.parse_args()
    __A         =	{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 6_4, """image_size""": 2_2_4}
    training_function(__lowercase			,				__lowercase    )


if __name__ == "__main__":
  main()












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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]    )						->      Dict:





    """simple docstring"""

    __A         =	3_8_4
    __A         =	7
    if "tiny" in model_name:
        __A         =	9_6
        __A         =	(2, 2, 6, 2)
        __A         =	(3, 6, 1_2, 2_4)
    elif "small" in model_name:
        __A         =	9_6
        __A         =	(2, 2, 1_8, 2)
        __A         =	(3, 6, 1_2, 2_4)
    elif "base" in model_name:
        __A         =	1_2_8
        __A         =	(2, 2, 1_8, 2)
        __A         =	(4, 8, 1_6, 3_2)
        __A         =	1_2
        __A         =	5_1_2
    elif "large" in model_name:
        __A         =	1_9_2
        __A         =	(2, 2, 1_8, 2)
        __A         =	(6, 1_2, 2_4, 4_8)
        __A         =	1_2
        __A         =	7_6_8

    # set label information
    __A         =	1_5_0
    __A         =	"""huggingface/label-files"""
    __A         =	"""ade20k-id2label.json"""
    __A         =	json.load(open(hf_hub_download(__lowercase			,				__lowercase			,				repo_type="""dataset"""    )			,				"""r"""    )    )
    __A         =	{int(__lowercase    ): v for k, v in idalabel.items()}
    __A         =	{v: k for k, v in idalabel.items()}

    __A         =	SwinConfig(
        embed_dim=__lowercase			,				depths=__lowercase			,				num_heads=__lowercase			,				window_size=__lowercase			,				out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]			,				)
    __A         =	UperNetConfig(
        backbone_config=__lowercase			,				auxiliary_in_channels=__lowercase			,				num_labels=__lowercase			,				idalabel=__lowercase			,				labelaid=__lowercase			,				)

    return config



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]			,				__lowercase   :			int			,				__lowercase   :			Optional[int]    )						->      Any:





    """simple docstring"""

    __A         =	dct.pop(__lowercase    )
    __A         =	val



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[str]			,				__lowercase   :			int    )						->      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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any    )						->      List[str]:





    """simple docstring"""

    __A    ,	__A         =	x.shape
    __A         =	x.reshape(__lowercase			,				4			,				in_channel // 4    )
    __A         =	x[:, [0, 2, 1, 3], :].transpose(1			,				2    ).reshape(__lowercase			,				__lowercase    )
    return x



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]    )						->      Dict:





    """simple docstring"""

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

    return x



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple    )						->      Tuple:





    """simple docstring"""

    __A         =	x.shape[0]
    __A         =	x.reshape(4			,				in_channel // 4    )
    __A         =	x[[0, 2, 1, 3], :].transpose(0			,				1    ).reshape(__lowercase    )
    return x



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]    )						->      Optional[Any]:





    """simple docstring"""

    __A         =	x.shape[0]
    __A         =	x.reshape(in_channel // 4			,				4    )
    __A         =	x[:, [0, 2, 1, 3]].transpose(0			,				1    ).reshape(__lowercase    )
    return x



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict			,				__lowercase   :			List[Any]			,				__lowercase   :			str    )						->      Tuple:





    """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(__lowercase			,				map_location="""cpu"""			,				file_name=__lowercase    )[
        """state_dict"""
    ]

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

    __A         =	get_upernet_config(__lowercase    )
    __A         =	UperNetForSemanticSegmentation(__lowercase    )
    model.eval()

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

    # rename keys
    __A         =	create_rename_keys(__lowercase    )
    for src, dest in rename_keys:
        rename_key(__lowercase			,				__lowercase			,				__lowercase    )
    read_in_q_k_v(__lowercase			,				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(__lowercase    )
            if "norm" in key:
                __A         =	reverse_correct_unfold_norm_order(__lowercase    )

    model.load_state_dict(__lowercase    )

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

    __A         =	SegformerImageProcessor()
    __A         =	processor(__lowercase			,				return_tensors="""pt"""    ).pixel_values

    with torch.no_grad():
        __A         =	model(__lowercase    )
        __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(
            [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]    )
    elif model_name == "upernet-swin-small":
        __A         =	torch.tensor(
            [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]    )
    elif model_name == "upernet-swin-base":
        __A         =	torch.tensor(
            [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]    )
    elif model_name == "upernet-swin-large":
        __A         =	torch.tensor(
            [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]    )
    print("""Logits:"""			,				outputs.logits[0, 0, :3, :3]    )
    assert torch.allclose(outputs.logits[0, 0, :3, :3]			,				__lowercase			,				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(__lowercase    )
        print(f"Saving processor to {pytorch_dump_folder_path}"    )
        processor.save_pretrained(__lowercase    )

    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      :       Union[str, Any]				  =   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."
  )

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




