






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()
snake_case__   :						List[str]													=   logging.get_logger(__name__)

snake_case__   :						str													=   torch.device("""cpu""")






def      _snake_case     ():
   UpperCamelCase_						=  'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase_						=  Image.open(requests.get(__lowercase     ,   stream=__lowercase).raw)
   return im






def      _snake_case     (__lowercase):
   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01])

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01])

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02])

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02])






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  dct.pop(__lowercase)
   UpperCamelCase_						=  val






def      _snake_case     (__lowercase):
   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      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   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(__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_						=  [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(__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, 1000])
   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__":
 snake_case__   :						Tuple													=   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.""")

 snake_case__   :						Tuple													=   parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









'''simple docstring'''



from __future__ import annotations

import os
from collections.abc import Mapping

A__						:   Optional[Any]				=    tuple[int, int]





class   UpperCAmelCase_    :





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         __lowerCamelCase  :      set[int]														=						vertices
         __lowerCamelCase  :      dict[EdgeT, int]														=						{
             (min(SCREAMING_SNAKE_CASE_      ), max(SCREAMING_SNAKE_CASE_      )): weight for edge, weight in edges.items()
         }







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         self.vertices.add(edge[0]      )
         self.vertices.add(edge[1]      )
         __lowerCamelCase  :      Union[str, Any]														=						weight







    def  lowercase_							(				self      )							->	Graph:
         __lowerCamelCase  :      Graph														=						Graph({min(self.vertices      )}     ,	{}      )
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int

         while len(subgraph.vertices      ) < len(self.vertices      ):
              __lowerCamelCase  :      Any														=						max(self.edges.values()      ) + 1
              for edge, weight in self.edges.items():
                   if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                        if weight < min_weight:
                             __lowerCamelCase  :      Optional[int]														=						edge
                             __lowerCamelCase  :      List[str]														=						weight

              subgraph.add_edge(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )

         return subgraph







def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "p107_network.txt"				)	->						int:
     __lowerCamelCase  :      str														=						os.path.abspath(os.path.dirname(UpperCAmelCase_				)				)
     __lowerCamelCase  :      str														=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				)
     __lowerCamelCase  :      dict[EdgeT, int]														=						{}
     __lowerCamelCase  :      list[str]
     __lowerCamelCase  :      int
     __lowerCamelCase  :      int

     with open(UpperCAmelCase_				) as f:
          __lowerCamelCase  :      Any														=						f.read().strip().split('\n'				)

     __lowerCamelCase  :      Any														=						[line.split(','				) for line in data]

     for edgea in range(1 ,       len(UpperCAmelCase_				)				):
          for edgea in range(UpperCAmelCase_				):
               if adjaceny_matrix[edgea][edgea] != "-":
                    __lowerCamelCase  :      int														=						int(adjaceny_matrix[edgea][edgea]				)

     __lowerCamelCase  :      Graph														=						Graph(set(range(len(UpperCAmelCase_				)				)				) ,       UpperCAmelCase_				)

     __lowerCamelCase  :      Graph														=						graph.prims_algorithm()

     __lowerCamelCase  :      int														=						sum(graph.edges.values()				)
     __lowerCamelCase  :      int														=						sum(subgraph.edges.values()				)

     return initial_total - optimal_total


if __name__ == "__main__":
      print(f'''{solution() = }''')






