

"""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()
UpperCAmelCase													=logging.get_logger(__name__)

UpperCAmelCase													=torch.device("cpu")



def   _A					(	):



      """simple docstring"""






      A				  =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      A				  =		Image.open(requests.get(_a     ,   stream=_a				).raw				)
      return im



def   _A					(	_a	:						str				):



      """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   _A					(	_a	:						List[Any]     ,   _a	:						Tuple     ,   _a	:						Optional[Any]				):



      """simple docstring"""






      A				  =		dct.pop(_a				)
      A				  =		val



def   _A					(	_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   _A					(	_a	:						int     ,   _a	:						Tuple     ,   _a	:						Tuple				):



      """simple docstring"""






      A				  =		SwiftFormerConfig()

      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      A				  =		1_0_0_0
      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				  =		[4_8, 5_6, 1_1_2, 2_2_0]

      elif swiftformer_name == "swiftformer_s":
            A				  =		[3, 3, 9, 6]
            A				  =		[4_8, 6_4, 1_6_8, 2_2_4]

      elif swiftformer_name == "swiftformer_l1":
            A				  =		[4, 3, 1_0, 5]
            A				  =		[4_8, 9_6, 1_9_2, 3_8_4]

      elif swiftformer_name == "swiftformer_l3":
            A				  =		[4, 4, 1_2, 6]
            A				  =		[6_4, 1_2_8, 3_2_0, 5_1_2]

      # 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, 1_0_0_0]				)
      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__":
  UpperCAmelCase													=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.")

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







"""simple docstring"""


import re

from filelock import FileLock


try:
  import nltk

  UpperCAmelCase													=True
except (ImportError, ModuleNotFoundError):
  UpperCAmelCase													=False

if NLTK_AVAILABLE:
  with FileLock(".lock") as lock:
    nltk.download("punkt", quiet=True)



def   _A					(	_a	:						str				):



      """simple docstring"""






      re.sub("""<n>"""     ,   """"""     ,   _a				)  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(_a				)				)




