

"""simple docstring"""





from importlib import import_module

from .logging import get_logger


_a       =							get_logger(__name__)




class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			Optional[int],					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			str=None	):
    __lowercase								=						attrs or []
    if module is not None:
       for key in module.__dict__:
          if key in attrs or not key.startswith("__"	):
             setattr(self,					UpperCAmelCase__,					getattr(UpperCAmelCase__,					UpperCAmelCase__	)	)
    __lowercase								=						module._original_module if isinstance(UpperCAmelCase__,					_PatchedModuleObj	) else module







class 					_lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase	:					Any   		=	[]


 def __init__(			self							:			List[Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			int=None	):
    __lowercase								=						obj
    __lowercase								=						target
    __lowercase								=						new
    __lowercase								=						target.split("."	)[0]
    __lowercase								=						{}
    __lowercase								=						attrs or []


 def __enter__(			self							:			List[str]	):
    *__lowercase		,__lowercase								=						self.target.split("."	)

    # Patch modules:
    # it's used to patch attributes of submodules like "os.path.join";
    # in this case we need to patch "os" and "os.path"

    for i in range(len(UpperCAmelCase__	)	):
       try:
          __lowercase								=						import_module(".".join(submodules[: i + 1]	)	)
       except ModuleNotFoundError:
          continue
       # We iterate over all the globals in self.obj in case we find "os" or "os.path"
       for attr in self.obj.__dir__():
          __lowercase								=						getattr(self.obj,					UpperCAmelCase__	)
          # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
          # This allows to patch renamed modules like "from os import path as ospath".
          if obj_attr is submodule or (
              (isinstance(UpperCAmelCase__,					_PatchedModuleObj	) and obj_attr._original_module is submodule)
          ):
             __lowercase								=						obj_attr
             # patch at top level
             setattr(self.obj,					UpperCAmelCase__,					_PatchedModuleObj(UpperCAmelCase__,					attrs=self.attrs	)	)
             __lowercase								=						getattr(self.obj,					UpperCAmelCase__	)
             # construct lower levels patches
             for key in submodules[i + 1 :]:
                setattr(UpperCAmelCase__,					UpperCAmelCase__,					_PatchedModuleObj(getattr(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	),					attrs=self.attrs	)	)
                __lowercase								=						getattr(UpperCAmelCase__,					UpperCAmelCase__	)
             # finally set the target attribute
             setattr(UpperCAmelCase__,					UpperCAmelCase__,					self.new	)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

    if submodules:  # if it's an attribute of a submodule like "os.path.join"
       try:
          __lowercase								=						getattr(import_module(".".join(UpperCAmelCase__	)	),					UpperCAmelCase__	)
       except (AttributeError, ModuleNotFoundError):
          return
       # We iterate over all the globals in self.obj in case we find "os.path.join"
       for attr in self.obj.__dir__():
          # We don't check for the name of the global, but rather if its value *is* "os.path.join".
          # This allows to patch renamed attributes like "from os.path import join as pjoin".
          if getattr(self.obj,					UpperCAmelCase__	) is attr_value:
             __lowercase								=						getattr(self.obj,					UpperCAmelCase__	)
             setattr(self.obj,					UpperCAmelCase__,					self.new	)
    elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
       __lowercase								=						globals()["__builtins__"][target_attr]
       setattr(self.obj,					UpperCAmelCase__,					self.new	)
    else:
       raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule."""	)


 def __exit__(			self							:			str,					*UpperCAmelCase__							:			Tuple	):
    for attr in list(self.original	):
       setattr(self.obj,					UpperCAmelCase__,					self.original.pop(UpperCAmelCase__	)	)


 def 					_lowercase (			self							:			Optional[int]	):
    self.__enter__()
    self._active_patches.append(self	)



 def 					_lowercase (			self							:			List[str]	):
    try:
       self._active_patches.remove(self	)
    except ValueError:
       # If the patch hasn't been started this will fail
       return None

    return self.__exit__()







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 			UpperCAmelCase__				(			):





  '''simple docstring'''







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




def 			UpperCAmelCase__				(			_A      : Optional[int]							):





  '''simple docstring'''







  if swiftformer_name == "swiftformer_xs":
    return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01]							)

  elif swiftformer_name == "swiftformer_s":
    return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01]							)

  elif swiftformer_name == "swiftformer_l1":
    return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02]							)

  elif swiftformer_name == "swiftformer_l3":
    return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02]							)




def 			UpperCAmelCase__				(			_A      : List[str]	,      _A      : Union[str, Any]	,      _A      : str							):





  '''simple docstring'''







  a__  =dct.pop(_A							)
  a__  =val




def 			UpperCAmelCase__				(			_A      : Any							):





  '''simple docstring'''







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




@torch.no_grad()
def 			UpperCAmelCase__				(			_A      : int	,      _A      : Union[str, Any]	,      _A      : int							):





  '''simple docstring'''







  a__  =SwiftFormerConfig()

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

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

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

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

  elif swiftformer_name == "swiftformer_l3":
    a__  =[4, 4, 12, 6]
    a__  =[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'''							):
      a__  =torch.hub.load_state_dict_from_url(_A	,      map_location='''cpu'''	,      check_hash=_A							)
    else:
      a__  =torch.load(_A	,      map_location='''cpu'''							)
  a__  =checkpoint

  a__  =create_rename_keys(_A							)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(_A	,      _A	,      _A							)

  # load HuggingFace model
  a__  =SwiftFormerForImageClassification(_A							).eval()
  hf_model.load_state_dict(_A							)

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

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

  assert hf_logits.shape == torch.Size([1, 10_00]							)
  assert torch.allclose(hf_logits[0, 0:5]	,      _A	,      atol=1E-3							)

  Path(_A							).mkdir(exist_ok=_A							)
  print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""							)
  hf_model.save_pretrained(_A							)


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)
