






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

SCREAMING_SNAKE_CASE__											=     torch.device('cpu')







def 					lowercase__				(   )->				Union[str, Any]:
      UpperCamelCase       						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      UpperCamelCase       						=      Image.open(requests.get(__UpperCamelCase	,		stream=__UpperCamelCase					).raw					)
      return im







def 					lowercase__				(   __UpperCamelCase					)->				List[Any]:
      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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				int:
      UpperCamelCase       						=      dct.pop(__UpperCamelCase					)
      UpperCamelCase       						=      val







def 					lowercase__				(   __UpperCamelCase					)->				str:
      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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Any:
      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(__UpperCamelCase	,		__UpperCamelCase	,		repo_type="""dataset"""					)	,		"""r"""					)					)
      UpperCamelCase       						=      {int(__UpperCamelCase					): 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(__UpperCamelCase	,		map_location="""cpu"""	,		check_hash=__UpperCamelCase					)
            else:
                  UpperCamelCase       						=      torch.load(__UpperCamelCase	,		map_location="""cpu"""					)
      UpperCamelCase       						=      checkpoint

      UpperCamelCase       						=      create_rename_keys(__UpperCamelCase					)
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)

      # load HuggingFace model
      UpperCamelCase       						=      SwiftFormerForImageClassification(__UpperCamelCase					).eval()
      hf_model.load_state_dict(__UpperCamelCase					)

      # prepare test inputs
      UpperCamelCase       						=      prepare_img()
      UpperCamelCase       						=      ViTImageProcessor.from_pretrained("""preprocessor_config"""					)
      UpperCamelCase       						=      processor(images=__UpperCamelCase	,		return_tensors="""pt"""					)

      # compare outputs from both models
      UpperCamelCase       						=      get_expected_output(__UpperCamelCase					)
      UpperCamelCase       						=      hf_model(inputs["""pixel_values"""]					).logits

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

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








'''simple docstring'''

def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				str:
      if not isinstance(__UpperCamelCase	,		__UpperCamelCase					):
            raise ValueError("""iterations must be defined as integers"""					)
      if not isinstance(__UpperCamelCase	,		__UpperCamelCase					) or not number >= 1:
            raise ValueError(
                """starting number must be
                         and integer and be more than 0"""					)
      if not iterations >= 1:
            raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz"""					)

      UpperCamelCase       						=      """"""
      while number <= iterations:
            if number % 3 == 0:
                  out += "Fizz"
            if number % 5 == 0:
                  out += "Buzz"
            if 0 not in (number % 3, number % 5):
                  out += str(__UpperCamelCase					)

            # print(out)
            number += 1
            out += " "
      return out


if __name__ == "__main__":
     import doctest

     doctest.testmod()
