


'''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_      :		str          =		logging.get_logger(__name__)

UpperCAmelCase_      :		Any          =		torch.device("cpu")


def    UpperCAmelCase_  (       ):
   '''simple docstring'''





   _a					:  Optional[int]						=  'http://images.cocodataset.org/val2017/000000039769.jpg'
   _a					:  Union[str, Any]						=  Image.open(requests.get(A		,	stream=A      ).raw      )
   return im


def    UpperCAmelCase_  (       A      ):
   '''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    UpperCAmelCase_  (       A		,	A		,	A      ):
   '''simple docstring'''





   _a					:  int						=  dct.pop(A      )
   _a					:  Optional[int]						=  val


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





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


@torch.no_grad()
def    UpperCAmelCase_  (       A		,	A		,	A      ):
   '''simple docstring'''





   _a					:  str						=  SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   _a					:  int						=  1_0_0_0
   _a					:  List[Any]						=  'huggingface/label-files'
   _a					:  str						=  'imagenet-1k-id2label.json'
   _a					:  Optional[int]						=  json.load(open(hf_hub_download(A		,	A		,	repo_type='dataset'      )		,	'r'      )      )
   _a					:  Any						=  {int(A      ): v for k, v in idalabel.items()}
   _a					:  Any						=  idalabel
   _a					:  Dict						=  {v: k for k, v in idalabel.items()}

   # size of the architecture
   if swiftformer_name == "swiftformer_xs":
      _a					:  str						=  [3, 3, 6, 4]
      _a					:  Dict						=  [4_8, 5_6, 1_1_2, 2_2_0]

   elif swiftformer_name == "swiftformer_s":
      _a					:  Tuple						=  [3, 3, 9, 6]
      _a					:  List[str]						=  [4_8, 6_4, 1_6_8, 2_2_4]

   elif swiftformer_name == "swiftformer_l1":
      _a					:  str						=  [4, 3, 1_0, 5]
      _a					:  Optional[Any]						=  [4_8, 9_6, 1_9_2, 3_8_4]

   elif swiftformer_name == "swiftformer_l3":
      _a					:  List[str]						=  [4, 4, 1_2, 6]
      _a					:  int						=  [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					:  Optional[int]						=  torch.hub.load_state_dict_from_url(A		,	map_location='cpu'		,	check_hash=A      )
      else:
         _a					:  Tuple						=  torch.load(A		,	map_location='cpu'      )
   _a					:  List[Any]						=  checkpoint

   _a					:  List[Any]						=  create_rename_keys(A      )
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(A		,	A		,	A      )

   # load HuggingFace model
   _a					:  Optional[Any]						=  SwiftFormerForImageClassification(A      ).eval()
   hf_model.load_state_dict(A      )

   # prepare test inputs
   _a					:  Optional[Any]						=  prepare_img()
   _a					:  Union[str, Any]						=  ViTImageProcessor.from_pretrained('preprocessor_config'      )
   _a					:  Optional[Any]						=  processor(images=A		,	return_tensors='pt'      )

   # compare outputs from both models
   _a					:  Optional[Any]						=  get_expected_output(A      )
   _a					:  Union[str, Any]						=  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_      :		Optional[int]          =		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_      :		Optional[int]          =		parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






'''simple docstring'''




import requests
from bsa import BeautifulSoup


def    UpperCAmelCase_  (       A = "https://www.worldometers.info/coronavirus"      ):
   '''simple docstring'''





   _a					:  Union[str, Any]						=  BeautifulSoup(requests.get(A      ).text		,	'html.parser'      )
   _a					:  int						=  soup.findAll('h1'      )
   _a					:  Union[str, Any]						=  soup.findAll('div'		,	{'class': 'maincounter-number'}      )
   keys += soup.findAll('span'		,	{'class': 'panel-title'}      )
   values += soup.findAll('div'		,	{'class': 'number-table-main'}      )
   return {key.text.strip(): value.text.strip() for key, value in zip(A		,	A      )}


if __name__ == "__main__":
 print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
 for key, value in world_covidaa_stats().items():
  print(f'''{key}\n{value}\n''')


