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

__magic_name__								=torch.device('''cpu''')







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     UpperCamelCase__										=      Image.open(requests.get(A ,							stream=A							).raw							)
     return im







def  __UpperCamelCase    (						A							):

     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 ,							A ,							A							):
     UpperCamelCase__										=      dct.pop(A							)
     UpperCamelCase__										=      val







def  __UpperCamelCase    (						A							):
     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  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      SwiftFormerConfig()

     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     UpperCamelCase__										=      1000
     UpperCamelCase__										=      '''huggingface/label-files'''
     UpperCamelCase__										=      '''imagenet-1k-id2label.json'''
     UpperCamelCase__										=      json.load(open(hf_hub_download(A ,							A ,							repo_type='''dataset'''							) ,							'''r'''							)							)
     UpperCamelCase__										=      {int(A							): 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, 112, 220]

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

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

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

     # 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(A ,							map_location='''cpu''' ,							check_hash=A							)
          else:
               UpperCamelCase__										=      torch.load(A ,							map_location='''cpu'''							)
     UpperCamelCase__										=      checkpoint

     UpperCamelCase__										=      create_rename_keys(A							)
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(A ,							A ,							A							)

     # load HuggingFace model
     UpperCamelCase__										=      SwiftFormerForImageClassification(A							).eval()
     hf_model.load_state_dict(A							)

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

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

     assert hf_logits.shape == torch.Size([1, 1000]							)
     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__":
     __magic_name__								=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.''')

     __magic_name__								=parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	List[str]						=""
  SCREAMING_SNAKE_CASE_	:	str						=(
      None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
  )
  SCREAMING_SNAKE_CASE_	:	str						=None  # compression type in fsspec. ex: "gzip"
  SCREAMING_SNAKE_CASE_	:	str						=None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz






  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = ""   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       super().__init__(self   ,				**SCREAMING_SNAKE_CASE_	)
       # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
       UpperCamelCase__										=      fsspec.open(
           SCREAMING_SNAKE_CASE_   ,				mode='''rb'''   ,				protocol=SCREAMING_SNAKE_CASE_   ,				compression=self.compression   ,				client_kwargs={
               '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
               '''trust_env''': True,  # Enable reading proxy env variables.
               **(target_options or {}).pop('''client_kwargs'''   ,				{}	),  # To avoid issues if it was already passed.
           }   ,				**(target_options or {})   ,				)
       UpperCamelCase__										=      os.path.basename(self.file.path.split('''::'''	)[0]	)
       UpperCamelCase__										=      (
           self.compressed_name[: self.compressed_name.rindex('''.'''	)]
           if '''.''' in self.compressed_name
           else self.compressed_name
       )
       UpperCamelCase__										=      None






  @classmethod
  def 		_a					(cls   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





       return super()._strip_protocol(SCREAMING_SNAKE_CASE_	).lstrip('''/'''	)






  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       if self.dir_cache is None:
            UpperCamelCase__										=      {**self.file.fs.info(self.file.path	), '''name''': self.uncompressed_name}
            UpperCamelCase__										=      {f['''name''']: f}






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       return self.file.open().read()






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = "rb"   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self._strip_protocol(SCREAMING_SNAKE_CASE_	)
       if mode != "rb":
            raise ValueError(F"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"	)
       return self.file.open()



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						="bz2"
  SCREAMING_SNAKE_CASE_	:	List[str]						="bz2"
  SCREAMING_SNAKE_CASE_	:	List[Any]						=".bz2"



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						="gzip"
  SCREAMING_SNAKE_CASE_	:	Dict						="gzip"
  SCREAMING_SNAKE_CASE_	:	List[Any]						=".gz"



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	List[Any]						="lz4"
  SCREAMING_SNAKE_CASE_	:	str						="lz4"
  SCREAMING_SNAKE_CASE_	:	Any						=".lz4"



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						="xz"
  SCREAMING_SNAKE_CASE_	:	List[str]						="xz"
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=".xz"



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						="zstd"
  SCREAMING_SNAKE_CASE_	:	Optional[int]						="zstd"
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=".zst"






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = "rb"   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = DEFAULT_BLOCK_SIZE   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      List[Any]:





       '''simple docstring'''





       super().__init__(
           fo=SCREAMING_SNAKE_CASE_   ,				mode=SCREAMING_SNAKE_CASE_   ,				target_protocol=SCREAMING_SNAKE_CASE_   ,				target_options=SCREAMING_SNAKE_CASE_   ,				block_size=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_   ,				)
       # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
       #
       # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
       #     out.close = close
       # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
       #
       # see https://github.com/intake/filesystem_spec/issues/725
       UpperCamelCase__										=      self.file.__enter__



       class     _A   :






         def __init__(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





              '''simple docstring'''





              UpperCamelCase__										=      file_






         def __enter__(self	)		->      Union[str, Any]:





              '''simple docstring'''





              self._file.__enter__()
              return self






         def __exit__(self   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      List[Any]:





              '''simple docstring'''





              self._file.__exit__(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)






         def __iter__(self	)		->      Dict:





              '''simple docstring'''





              return iter(self._file	)






         def 		_a					(self	)		->      Dict:





              '''simple docstring'''





              return next(self._file	)






         def __getattr__(self   ,				SCREAMING_SNAKE_CASE_	)		->      str:





              '''simple docstring'''





              return getattr(self._file   ,				SCREAMING_SNAKE_CASE_	)

       def fixed_enter(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	):
            return WrappedFile(_enter(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)	)

       UpperCamelCase__										=      fixed_enter



