





import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A_												=     logging.get_logger(__name__)

A_												=     torch.device("cpu")





def    __UpperCamelCase      (    ) ->Union[str, Any]:
					lowerCamelCase__       =       "http://images.cocodataset.org/val2017/000000039769.jpg"
					lowerCamelCase__       =       Image.open(requests.get(__UpperCamelCase,     stream=__UpperCamelCase).raw)
					return im





def    __UpperCamelCase      (    a) ->List[Any]:
					if swiftformer_name == "swiftformer_xs":
										return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01])

					elif swiftformer_name == "swiftformer_s":
										return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01])

					elif swiftformer_name == "swiftformer_l1":
										return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02])

					elif swiftformer_name == "swiftformer_l3":
										return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02])





def    __UpperCamelCase      (    a,     a,     a) ->int:
					lowerCamelCase__       =       dct.pop(__UpperCamelCase)
					lowerCamelCase__       =       val





def    __UpperCamelCase      (    a) ->str:
					lowerCamelCase__       =       []
					for k in state_dict.keys():
										lowerCamelCase__       =       k
										if ".pwconv" in k:
															lowerCamelCase__       =       k_new.replace(".pwconv",     ".point_wise_conv")
										if ".dwconv" in k:
															lowerCamelCase__       =       k_new.replace(".dwconv",     ".depth_wise_conv")
										if ".Proj." in k:
															lowerCamelCase__       =       k_new.replace(".Proj.",     ".proj.")
										if "patch_embed" in k_new:
															lowerCamelCase__       =       k_new.replace("patch_embed",     "swiftformer.patch_embed.patch_embedding")
										if "network" in k_new:
															lowerCamelCase__       =       k_new.split(".")
															if ls[2].isdigit():
																				lowerCamelCase__       =       "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:])
															else:
																				lowerCamelCase__       =       k_new.replace("network",     "swiftformer.encoder.network")
										rename_keys.append((k, k_new))
					return rename_keys





@torch.no_grad()
def    __UpperCamelCase      (    a,     a,     a) ->Any:
					lowerCamelCase__       =       SwiftFormerConfig()

					# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
					lowerCamelCase__       =       1000
					lowerCamelCase__       =       "huggingface/label-files"
					lowerCamelCase__       =       "imagenet-1k-id2label.json"
					lowerCamelCase__       =       json.load(open(hf_hub_download(__UpperCamelCase,     __UpperCamelCase,     repo_type="dataset"),     "r"))
					lowerCamelCase__       =       {int(__UpperCamelCase): v for k, v in idalabel.items()}
					lowerCamelCase__       =       idalabel
					lowerCamelCase__       =       {v: k for k, v in idalabel.items()}

					# size of the architecture
					if swiftformer_name == "swiftformer_xs":
										lowerCamelCase__       =       [3, 3, 6, 4]
										lowerCamelCase__       =       [48, 56, 112, 220]

					elif swiftformer_name == "swiftformer_s":
										lowerCamelCase__       =       [3, 3, 9, 6]
										lowerCamelCase__       =       [48, 64, 168, 224]

					elif swiftformer_name == "swiftformer_l1":
										lowerCamelCase__       =       [4, 3, 10, 5]
										lowerCamelCase__       =       [48, 96, 192, 384]

					elif swiftformer_name == "swiftformer_l3":
										lowerCamelCase__       =       [4, 4, 12, 6]
										lowerCamelCase__       =       [64, 128, 320, 512]

					# load state_dict of original model, remove and rename some keys
					if original_ckpt:
										if original_ckpt.startswith("https"):
															lowerCamelCase__       =       torch.hub.load_state_dict_from_url(__UpperCamelCase,     map_location="cpu",     check_hash=__UpperCamelCase)
										else:
															lowerCamelCase__       =       torch.load(__UpperCamelCase,     map_location="cpu")
					lowerCamelCase__       =       checkpoint

					lowerCamelCase__       =       create_rename_keys(__UpperCamelCase)
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(__UpperCamelCase,     __UpperCamelCase,     __UpperCamelCase)

					# load HuggingFace model
					lowerCamelCase__       =       SwiftFormerForImageClassification(__UpperCamelCase).eval()
					hf_model.load_state_dict(__UpperCamelCase)

					# prepare test inputs
					lowerCamelCase__       =       prepare_img()
					lowerCamelCase__       =       ViTImageProcessor.from_pretrained("preprocessor_config")
					lowerCamelCase__       =       processor(images=__UpperCamelCase,     return_tensors="pt")

					# compare outputs from both models
					lowerCamelCase__       =       get_expected_output(__UpperCamelCase)
					lowerCamelCase__       =       hf_model(inputs["pixel_values"]).logits

					assert hf_logits.shape == torch.Size([1, 1000])
					assert torch.allclose(hf_logits[0, 0:5],     __UpperCamelCase,     atol=1E-3)

					Path(__UpperCamelCase).mkdir(exist_ok=__UpperCamelCase)
					print(f"Saving model {swiftformer_name} to {pytorch_dump_folder_path}")
					hf_model.save_pretrained(__UpperCamelCase)


if __name__ == "__main__":
		A_												=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--swiftformer_name",
		    default="swiftformer_xs",
		    choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
		    type=str,
		    help="Name of the SwiftFormer model you\'d like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path",
		    default="./converted_outputs/",
		    type=str,
		    help="Path to the output PyTorch model directory.",
		)
		parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

		A_												=     parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def    __UpperCamelCase      (    a,     a=False) ->Optional[Any]:
					lowerCamelCase__       =       OmegaConf.load(a)
					if display:
										print(yaml.dump(OmegaConf.to_container(a)))
					return config





def    __UpperCamelCase      (    a,     a=None,     a=None) ->List[Any]:
					if conf_path is None:
										lowerCamelCase__       =       "./model_checkpoints/vqgan_only.yaml"
					lowerCamelCase__       =       load_config(a,     display=a)
					lowerCamelCase__       =       VQModel(**config.model.params)
					if ckpt_path is None:
										lowerCamelCase__       =       "./model_checkpoints/vqgan_only.pt"
					lowerCamelCase__       =       torch.load(a,     map_location=a)
					if ".ckpt" in ckpt_path:
										lowerCamelCase__       =       sd["state_dict"]
					model.load_state_dict(a,     strict=a)
					model.to(a)
					del sd
					return model





def    __UpperCamelCase      (    a,     a) ->int:
					lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       model.encode(a)
					print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}")
					lowerCamelCase__       =       model.decode(a)
					return xrec





def    __UpperCamelCase      (    a,     a=False) ->Dict:
					lowerCamelCase__    ,		lowerCamelCase__       =       string.rsplit(".",     1)
					if reload:
										lowerCamelCase__       =       importlib.import_module(a)
										importlib.reload(a)
					return getattr(importlib.import_module(a,     package=a),     cls)





def    __UpperCamelCase      (    a) ->int:
					if "target" not in config:
										raise KeyError("Expected key `target` to instantiate.")
					return get_obj_from_str(config["target"])(**config.get("params",     {}))





def    __UpperCamelCase      (    a,     a,     a=True,     a=True) ->Optional[Any]:
					lowerCamelCase__       =       instantiate_from_config(a)
					if sd is not None:
										model.load_state_dict(a)
					if gpu:
										model.cuda()
					if eval_mode:
										model.eval()
					return {"model": model}







def    __UpperCamelCase      (    a,     a,     a,     a) ->Dict:
					# load the specified checkpoint
					if ckpt:
										lowerCamelCase__       =       torch.load(a,     map_location="cpu")
										lowerCamelCase__       =       pl_sd["global_step"]
										print(f"loaded model from global step {global_step}.")
					else:
										lowerCamelCase__       =       {"state_dict": None}
										lowerCamelCase__       =       None
					lowerCamelCase__       =       load_model_from_config(config.model,     pl_sd["state_dict"],     gpu=a,     eval_mode=a)["model"]
					return model, global_step

