





'''simple docstring'''

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()
__lowerCamelCase : Optional[int]											=			logging.get_logger(__name__)

__lowerCamelCase : Tuple											=			torch.device("cpu")







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = "http://images.cocodataset.org/val2017/000000039769.jpg"
				lowercase         = Image.open(requests.get(lowerCAmelCase_		,							stream=lowerCAmelCase_							).raw							)
				return im







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				if swiftformer_name == "swiftformer_xs":
								return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]							)

				elif swiftformer_name == "swiftformer_s":
								return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]							)

				elif swiftformer_name == "swiftformer_l1":
								return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]							)

				elif swiftformer_name == "swiftformer_l3":
								return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]							)







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = dct.pop(lowerCAmelCase_							)
				lowercase         = val







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






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







@torch.no_grad()
def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = SwiftFormerConfig()

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

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

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

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

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

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

				lowercase         = create_rename_keys(lowerCAmelCase_							)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							)

				# load HuggingFace model
				lowercase         = SwiftFormerForImageClassification(lowerCAmelCase_							).eval()
				hf_model.load_state_dict(lowerCAmelCase_							)

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

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

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

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


if __name__ == "__main__":
			__lowerCamelCase : str											=			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.")

			__lowerCamelCase : Union[str, Any]											=			parser.parse_args()
			convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)











'''simple docstring'''

# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


__lowerCamelCase : Any											=			open  # noqa: we just need to have a builtin inside this module to test it properly




