





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()
__lowercase	: Union[str, Any]       =logging.get_logger(__name__)

__lowercase	: List[Any]       =torch.device("""cpu""")




def      a__			(       ):






   '''simple docstring'''






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




def      a__			(       lowercase__	):






   '''simple docstring'''






   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]	)

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]	)

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]	)

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]	)




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =dct.pop(lowercase__	)
   UpperCAmelCase_           =val




def      a__			(       lowercase__	):






   '''simple docstring'''






   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      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   UpperCAmelCase_           =1_0_0_0
   UpperCAmelCase_           ="huggingface/label-files"
   UpperCAmelCase_           ="imagenet-1k-id2label.json"
   UpperCAmelCase_           =json.load(open(hf_hub_download(lowercase__	,     lowercase__	,     repo_type="dataset"	)	,     "r"	)	)
   UpperCAmelCase_           ={int(lowercase__	): 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_           =[4_8, 5_6, 1_1_2, 2_2_0]

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

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

   elif swiftformer_name == "swiftformer_l3":
      UpperCAmelCase_           =[4, 4, 1_2, 6]
      UpperCAmelCase_           =[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"	):
         UpperCAmelCase_           =torch.hub.load_state_dict_from_url(lowercase__	,     map_location="cpu"	,     check_hash=lowercase__	)
      else:
         UpperCAmelCase_           =torch.load(lowercase__	,     map_location="cpu"	)
   UpperCAmelCase_           =checkpoint

   UpperCAmelCase_           =create_rename_keys(lowercase__	)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(lowercase__	,     lowercase__	,     lowercase__	)

   # load HuggingFace model
   UpperCAmelCase_           =SwiftFormerForImageClassification(lowercase__	).eval()
   hf_model.load_state_dict(lowercase__	)

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

   # compare outputs from both models
   UpperCAmelCase_           =get_expected_output(lowercase__	)
   UpperCAmelCase_           =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]	,     lowercase__	,     atol=1E-3	)

   Path(lowercase__	).mkdir(exist_ok=lowercase__	)
   print(F'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'	)
   hf_model.save_pretrained(lowercase__	)


if __name__ == "__main__":
     __lowercase	: 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.""")

     __lowercase	: List[Any]       =parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









import requests

_lowerCamelCase    :		int		      =    """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""

def        __a			(   __lowerCAmelCase   )     ->     None:
					# fetching a list of articles in json format
					SCREAMING_SNAKE_CASE							:     List[str]						=				requests.get(_NEWS_API + bbc_news_api_key   ).json()
					# each article in the list is a dict
					for i, article in enumerate(bbc_news_page['articles']      ,	1   ):
										print(F'''{i}.) {article['title']}'''   )


if __name__ == "__main__":
			fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")