





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_ (       )     ->		Dict:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowerCamelCase__:      Tuple			  =Image.open(requests.get(__a	,    stream=__a		).raw		)
  return im





def      lowerCAmelCase_ (       __a		)     ->		Optional[int]:







  """simple docstring"""





  if swiftformer_name == "swiftformer_xs":
    return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]		)

  elif swiftformer_name == "swiftformer_s":
    return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]		)

  elif swiftformer_name == "swiftformer_l1":
    return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]		)

  elif swiftformer_name == "swiftformer_l3":
    return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      int			  =dct.pop(__a		)
  lowerCamelCase__:      Any			  =val





def      lowerCAmelCase_ (       __a		)     ->		Dict:







  """simple docstring"""





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





@torch.no_grad()
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  =SwiftFormerConfig()

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

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

  elif swiftformer_name == "swiftformer_s":
    lowerCamelCase__:      Dict			  =[3, 3, 9, 6]
    lowerCamelCase__:      Union[str, Any]			  =[48, 64, 168, 224]

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

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

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

  lowerCamelCase__:      Dict			  =create_rename_keys(__a		)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(__a	,    __a	,    __a		)

  # load HuggingFace model
  lowerCamelCase__:      List[Any]			  =SwiftFormerForImageClassification(__a		).eval()
  hf_model.load_state_dict(__a		)

  # prepare test inputs
  lowerCamelCase__:      Dict			  =prepare_img()
  lowerCamelCase__:      Tuple			  =ViTImageProcessor.from_pretrained("preprocessor_config"		)
  lowerCamelCase__:      Optional[int]			  =processor(images=__a	,    return_tensors="pt"		)

  # compare outputs from both models
  lowerCamelCase__:      Optional[int]			  =get_expected_output(__a		)
  lowerCamelCase__:      List[str]			  =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__":
     __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)






"""simple docstring"""
import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


A:   str								=		logging.get_logger(__name__)

A:   List[Any]								=		{"vocab_file": "vocab.txt"}

A:   List[str]								=		{
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

A:   Dict								=		{
    "facebook/esm2_t6_8M_UR50D": 1_0_2_4,
    "facebook/esm2_t12_35M_UR50D": 1_0_2_4,
}

def       _snake_case						(						UpperCamelCase						:		int						):
 with open(UpperCamelCase			,		"""r"""						) as f:
  UpperCAmelCase	:					int	=	f.read().splitlines()
  return [l.strip() for l in lines]



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Union[str, Any]						=						VOCAB_FILES_NAMES
   __lowerCAmelCase  :   Dict						=						PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase  :   List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase  :   str						=						['input_ids', 'attention_mask']

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE="<unk>"      ,     _SCREAMING_SNAKE_CASE="<cls>"      ,     _SCREAMING_SNAKE_CASE="<pad>"      ,     _SCREAMING_SNAKE_CASE="<mask>"      ,     _SCREAMING_SNAKE_CASE="<eos>"      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Any:





    '''simple docstring'''




    super().__init__(**_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	load_vocab_file(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	dict(enumerate(self.all_tokens						)						)
    UpperCAmelCase	:					List[str]	=	{tok: ind for ind, tok in enumerate(self.all_tokens						)}
    UpperCAmelCase	:					Any	=	unk_token
    UpperCAmelCase	:					str	=	cls_token
    UpperCAmelCase	:					int	=	pad_token
    UpperCAmelCase	:					Tuple	=	mask_token
    UpperCAmelCase	:					str	=	eos_token
    UpperCAmelCase	:					List[str]	=	self.all_tokens
    self._create_trie(self.unique_no_split_tokens						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,     self.unk_token						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,     self._token_to_id.get(self.unk_token						)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    return text.split()

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=False						)  ->      Union[str, Any]:





    '''simple docstring'''




    return len(self._id_to_token						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    return {token: i for i, token in enumerate(self.all_tokens						)}

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,     self._token_to_id.get(self.unk_token						)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,     self.unk_token						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	[self.cls_token_id]
    UpperCAmelCase	:					Tuple	=	[self.eos_token_id]  # No sep token in ESM vocabulary
    if token_ids_a is None:
     if self.eos_token_id is None:
      return cls + token_ids_a
     else:
      return cls + token_ids_a + sep
    elif self.eos_token_id is None:
     raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!"""						)
    return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = False						)  ->      List[int]:





    '''simple docstring'''




    if already_has_special_tokens:
     if token_ids_a is not None:
      raise ValueError(
          """You should not supply a second sequence if the provided sequence of """
          """ids is already formatted with special tokens for the model."""						)

     return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
    UpperCAmelCase	:					str	=	[1] + ([0] * len(_SCREAMING_SNAKE_CASE						)) + [1]
    if token_ids_a is not None:
     mask += [0] * len(_SCREAMING_SNAKE_CASE						) + [1]
    return mask

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	os.path.join(_SCREAMING_SNAKE_CASE      ,     (filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""						)
    with open(_SCREAMING_SNAKE_CASE      ,     """w"""						) as f:
     f.write("""\n""".join(self.all_tokens						)						)
    return (vocab_file,)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return self.get_vocab_size(with_added_tokens=_SCREAMING_SNAKE_CASE						)




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = False						)  ->      int:





    '''simple docstring'''




    return super()._add_tokens(_SCREAMING_SNAKE_CASE      ,     special_tokens=_SCREAMING_SNAKE_CASE						)
