'''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()
a_							=      logging.get_logger(__name__)

a_							=      torch.device("""cpu""")




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




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




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




@torch.no_grad()
def       __lowerCAmelCase      (						A_   : Optional[Any]						,	A_   : Union[str, Any]						,	A_   : Tuple					)    ->  Any:
  __UpperCAmelCase						    =       SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  __UpperCAmelCase						    =       10_00
  __UpperCAmelCase						    =       "huggingface/label-files"
  __UpperCAmelCase						    =       "imagenet-1k-id2label.json"
  __UpperCAmelCase						    =       json.load(open(hf_hub_download(__UpperCamelCase						,	__UpperCamelCase						,	repo_type="dataset"					)						,	"r"					)					)
  __UpperCAmelCase						    =       {int(__UpperCamelCase					): v for k, v in idalabel.items()}
  __UpperCAmelCase						    =       idalabel
  __UpperCAmelCase						    =       {v: k for k, v in idalabel.items()}

  # size of the architecture
  if swiftformer_name == "swiftformer_xs":
    __UpperCAmelCase						    =       [3, 3, 6, 4]
    __UpperCAmelCase						    =       [48, 56, 1_12, 2_20]

  elif swiftformer_name == "swiftformer_s":
    __UpperCAmelCase						    =       [3, 3, 9, 6]
    __UpperCAmelCase						    =       [48, 64, 1_68, 2_24]

  elif swiftformer_name == "swiftformer_l1":
    __UpperCAmelCase						    =       [4, 3, 10, 5]
    __UpperCAmelCase						    =       [48, 96, 1_92, 3_84]

  elif swiftformer_name == "swiftformer_l3":
    __UpperCAmelCase						    =       [4, 4, 12, 6]
    __UpperCAmelCase						    =       [64, 1_28, 3_20, 5_12]

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

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

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

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

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

  assert hf_logits.shape == torch.Size([1, 10_00]					)
  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)




from ..utils import DummyObject, requires_backends


class 			UpperCAmelCase__    (		metaclass=snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	List[str]    		= ['transformers', 'torch', 'note_seq']






  def __init__(				self:			List[str]				,				*__lowerCAmelCase:			Optional[int]				,				**__lowerCAmelCase:			List[Any]   ) -> Optional[int]:




    '''simple docstring'''




    requires_backends(self				,				["transformers", "torch", "note_seq"]   )






  @classmethod
  def 	_UpperCAmelCase				(				cls:			Optional[int]				,				*__lowerCAmelCase:			Any				,				**__lowerCAmelCase:			List[str]   ) -> Dict:




    '''simple docstring'''




    requires_backends(cls				,				["transformers", "torch", "note_seq"]   )






  @classmethod
  def 	_UpperCAmelCase				(				cls:			Union[str, Any]				,				*__lowerCAmelCase:			Optional[Any]				,				**__lowerCAmelCase:			Optional[Any]   ) -> Any:




    '''simple docstring'''




    requires_backends(cls				,				["transformers", "torch", "note_seq"]   )



