

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 				snake_case_()							->     Dict:



  """simple docstring"""






  _snake_case						=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case						=							Image.open(requests.get(_UpperCamelCase			,	stream=_UpperCamelCase					).raw					)
  return im



def 				snake_case_(_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






  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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Any:



  """simple docstring"""






  _snake_case						=							dct.pop(_UpperCamelCase					)
  _snake_case						=							val



def 				snake_case_(_UpperCamelCase					)							->     Union[str, Any]:



  """simple docstring"""






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



@torch.no_grad()
def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     str:



  """simple docstring"""






  _snake_case						=							SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  _snake_case						=							1_000
  _snake_case						=							'''huggingface/label-files'''
  _snake_case						=							'''imagenet-1k-id2label.json'''
  _snake_case						=							json.load(open(hf_hub_download(_UpperCamelCase			,	_UpperCamelCase			,	repo_type='''dataset'''					)			,	'''r'''					)					)
  _snake_case						=							{int(_UpperCamelCase					): v for k, v in idalabel.items()}
  _snake_case						=							idalabel
  _snake_case						=							{v: k for k, v in idalabel.items()}

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

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

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

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

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

  _snake_case						=							create_rename_keys(_UpperCamelCase					)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)

  # load HuggingFace model
  _snake_case						=							SwiftFormerForImageClassification(_UpperCamelCase					).eval()
  hf_model.load_state_dict(_UpperCamelCase					)

  # prepare test inputs
  _snake_case						=							prepare_img()
  _snake_case						=							ViTImageProcessor.from_pretrained('''preprocessor_config'''					)
  _snake_case						=							processor(images=_UpperCamelCase			,	return_tensors='''pt'''					)

  # compare outputs from both models
  _snake_case						=							get_expected_output(_UpperCamelCase					)
  _snake_case						=							hf_model(inputs['''pixel_values''']					).logits

  assert hf_logits.shape == torch.Size([1, 1_000]					)
  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)




def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     str:



  """simple docstring"""






  if a < 0 or b < 0:
    raise ValueError('''the value of both inputs must be positive'''					)
  _snake_case						=							str(bin(_UpperCamelCase					)					)[2:]  # remove the leading "0b"
  _snake_case						=							str(bin(_UpperCamelCase					)					)[2:]
  _snake_case						=							max(len(_UpperCamelCase					)			,	len(_UpperCamelCase					)					)
  return "0b" + "".join(
      str(int('''1''' in (char_a, char_b)					)					)
      for char_a, char_b in zip(a_binary.zfill(_UpperCamelCase					)			,	b_binary.zfill(_UpperCamelCase					)					)					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

