




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_					    = logging.get_logger(__name__)

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



def      __lowerCAmelCase			(					)     ->	str:
  lowerCAmelCase__				:  List[Any]		       =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  lowerCAmelCase__				:  Tuple		       =     Image.open(requests.get(UpperCamelCase	,      stream=UpperCamelCase      ).raw      )
  return im



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Dict:
  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			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Optional[Any]:
  lowerCAmelCase__				:  Union[str, Any]		       =     dct.pop(UpperCamelCase      )
  lowerCAmelCase__				:  List[str]		       =     val



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



@torch.no_grad()
def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Optional[Any]:
  lowerCAmelCase__				:  int		       =     SwiftFormerConfig()

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

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

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

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

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

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

  lowerCAmelCase__				:  Dict		       =     create_rename_keys(UpperCamelCase      )
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )

  # load HuggingFace model
  lowerCAmelCase__				:  Union[str, Any]		       =     SwiftFormerForImageClassification(UpperCamelCase      ).eval()
  hf_model.load_state_dict(UpperCamelCase      )

  # prepare test inputs
  lowerCAmelCase__				:  Tuple		       =     prepare_img()
  lowerCAmelCase__				:  str		       =     ViTImageProcessor.from_pretrained('''preprocessor_config'''      )
  lowerCAmelCase__				:  Union[str, Any]		       =     processor(images=UpperCamelCase	,      return_tensors='''pt'''      )

  # compare outputs from both models
  lowerCAmelCase__				:  str		       =     get_expected_output(UpperCamelCase      )
  lowerCAmelCase__				:  Dict		       =     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__":
     lowerCAmelCase_					    = 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_					    = parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







from datetime import datetime

import matplotlib.pyplot as plt
import torch



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	str:
  for param in module.parameters():
    lowerCAmelCase__				:  int		       =     False



def      __lowerCAmelCase			(					)     ->	Optional[Any]:
  lowerCAmelCase__				:  List[Any]		       =     '''cuda''' if torch.cuda.is_available() else '''cpu'''
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    lowerCAmelCase__				:  Optional[Any]		       =     '''mps'''
  if device == "mps":
    print(
        '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
        ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
        ''' with generations.'''      )
  return device



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     plt.imshow(UpperCamelCase      )
  fig.axes.get_xaxis().set_visible(UpperCamelCase      )
  fig.axes.get_yaxis().set_visible(UpperCamelCase      )
  plt.show()



def      __lowerCAmelCase			(					)     ->	str:
  lowerCAmelCase__				:  Dict		       =     datetime.now()
  lowerCAmelCase__				:  Optional[int]		       =     current_time.strftime('''%H:%M:%S'''      )
  return timestamp

