





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       :     List[Any]   =     logging.get_logger(__name__)

__lowerCamelCase       :     Optional[int]   =     torch.device("""cpu""")



def 		A_				(     )						-> Optional[int]:
 UpperCamelCase	:					Optional[Any]       	=   "http://images.cocodataset.org/val2017/000000039769.jpg"
 UpperCamelCase	:					Tuple       	=   Image.open(requests.get(_lowerCAmelCase							,   stream=_lowerCAmelCase     ).raw     )
 return im



def 		A_				(     _lowerCAmelCase     )						-> str:
 if swiftformer_name == "swiftformer_xs":
  return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]     )

 elif swiftformer_name == "swiftformer_s":
  return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]     )

 elif swiftformer_name == "swiftformer_l1":
  return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]     )

 elif swiftformer_name == "swiftformer_l3":
  return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[Any]:
 UpperCamelCase	:					Optional[int]       	=   dct.pop(_lowerCAmelCase     )
 UpperCamelCase	:					Union[str, Any]       	=   val



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



@torch.no_grad()
def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[Any]:
 UpperCamelCase	:					Tuple       	=   SwiftFormerConfig()

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

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

 elif swiftformer_name == "swiftformer_s":
  UpperCamelCase	:					Optional[int]       	=   [3, 3, 9, 6]
  UpperCamelCase	:					str       	=   [48, 64, 168, 224]

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

 elif swiftformer_name == "swiftformer_l3":
  UpperCamelCase	:					Optional[int]       	=   [4, 4, 12, 6]
  UpperCamelCase	:					str       	=   [64, 128, 320, 512]

 # load state_dict of original model, remove and rename some keys
 if original_ckpt:
  if original_ckpt.startswith("https"     ):
   UpperCamelCase	:					Optional[Any]       	=   torch.hub.load_state_dict_from_url(_lowerCAmelCase							,   map_location="cpu"							,   check_hash=_lowerCAmelCase     )
  else:
   UpperCamelCase	:					str       	=   torch.load(_lowerCAmelCase							,   map_location="cpu"     )
 UpperCamelCase	:					Dict       	=   checkpoint

 UpperCamelCase	:					List[str]       	=   create_rename_keys(_lowerCAmelCase     )
 for rename_key_src, rename_key_dest in rename_keys:
  rename_key(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )

 # load HuggingFace model
 UpperCamelCase	:					Any       	=   SwiftFormerForImageClassification(_lowerCAmelCase     ).eval()
 hf_model.load_state_dict(_lowerCAmelCase     )

 # prepare test inputs
 UpperCamelCase	:					Dict       	=   prepare_img()
 UpperCamelCase	:					int       	=   ViTImageProcessor.from_pretrained("preprocessor_config"     )
 UpperCamelCase	:					List[Any]       	=   processor(images=_lowerCAmelCase							,   return_tensors="pt"     )

 # compare outputs from both models
 UpperCamelCase	:					List[str]       	=   get_expected_output(_lowerCAmelCase     )
 UpperCamelCase	:					Any       	=   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       :     Union[str, Any]   =     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       :     List[str]   =     parser.parse_args()
    convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Optional[int]       	=   int(_lowerCAmelCase     )
 if decimal in (0, 1):  # Exit cases for the recursion
  return str(_lowerCAmelCase     )
 UpperCamelCase	,    UpperCamelCase	:					Dict       	=   divmod(_lowerCAmelCase							,   2     )
 return binary_recursive(_lowerCAmelCase     ) + str(_lowerCAmelCase     )



def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Tuple       	=   str(_lowerCAmelCase     ).strip()
 if not number:
  raise ValueError("No input value was provided"     )
 UpperCamelCase	:					Optional[int]       	=   "-" if number.startswith("-"     ) else ""
 UpperCamelCase	:					Any       	=   number.lstrip("-"     )
 if not number.isnumeric():
  raise ValueError("Input value is not an integer"     )
 return F"""{negative}0b{binary_recursive(int(_lowerCAmelCase     )     )}"""


if __name__ == "__main__":
    from doctest import testmod

    testmod()
