





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()
__A     =    logging.get_logger(__name__)

__A     =    torch.device("cpu")





def 					lowerCAmelCase_			(	)					->		Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:				Any        ="http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase__:				int        =Image.open(requests.get(__a     ,   stream=__a		).raw		)
  return im





def 					lowerCAmelCase_			(	__a		)					->		Union[str, Any]:







  """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 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		str:







  """simple docstring"""





  lowerCamelCase__:				Dict        =dct.pop(__a		)
  lowerCamelCase__:				Optional[int]        =val





def 					lowerCAmelCase_			(	__a		)					->		Optional[Any]:







  """simple docstring"""





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





@torch.no_grad()
def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		List[str]:







  """simple docstring"""





  lowerCamelCase__:				str        =SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  lowerCamelCase__:				int        =1000
  lowerCamelCase__:				Optional[Any]        ="huggingface/label-files"
  lowerCamelCase__:				Optional[int]        ="imagenet-1k-id2label.json"
  lowerCamelCase__:				Optional[int]        =json.load(open(hf_hub_download(__a     ,   __a     ,   repo_type="dataset"		)     ,   "r"		)		)
  lowerCamelCase__:				List[Any]        ={int(__a		): v for k, v in idalabel.items()}
  lowerCamelCase__:				Any        =idalabel
  lowerCamelCase__:				Optional[int]        ={v: k for k, v in idalabel.items()}

  # size of the architecture
  if swiftformer_name == "swiftformer_xs":
    lowerCamelCase__:				Optional[Any]        =[3, 3, 6, 4]
    lowerCamelCase__:				Any        =[48, 56, 112, 220]

  elif swiftformer_name == "swiftformer_s":
    lowerCamelCase__:				int        =[3, 3, 9, 6]
    lowerCamelCase__:				Union[str, Any]        =[48, 64, 168, 224]

  elif swiftformer_name == "swiftformer_l1":
    lowerCamelCase__:				Union[str, Any]        =[4, 3, 10, 5]
    lowerCamelCase__:				List[str]        =[48, 96, 192, 384]

  elif swiftformer_name == "swiftformer_l3":
    lowerCamelCase__:				Any        =[4, 4, 12, 6]
    lowerCamelCase__:				Union[str, Any]        =[64, 128, 320, 512]

  # load state_dict of original model, remove and rename some keys
  if original_ckpt:
    if original_ckpt.startswith("https"		):
      lowerCamelCase__:				Dict        =torch.hub.load_state_dict_from_url(__a     ,   map_location="cpu"     ,   check_hash=__a		)
    else:
      lowerCamelCase__:				Optional[int]        =torch.load(__a     ,   map_location="cpu"		)
  lowerCamelCase__:				Optional[int]        =checkpoint

  lowerCamelCase__:				Tuple        =create_rename_keys(__a		)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(__a     ,   __a     ,   __a		)

  # load HuggingFace model
  lowerCamelCase__:				List[Any]        =SwiftFormerForImageClassification(__a		).eval()
  hf_model.load_state_dict(__a		)

  # prepare test inputs
  lowerCamelCase__:				str        =prepare_img()
  lowerCamelCase__:				Tuple        =ViTImageProcessor.from_pretrained("preprocessor_config"		)
  lowerCamelCase__:				str        =processor(images=__a     ,   return_tensors="pt"		)

  # compare outputs from both models
  lowerCamelCase__:				List[Any]        =get_expected_output(__a		)
  lowerCamelCase__:				str        =hf_model(inputs["pixel_values"]		).logits

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

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








from __future__ import annotations





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		List[Any]:







  """simple docstring"""





  print(F"""Vertex\tShortest Distance from vertex {src}"""		)
  for i, d in enumerate(__a		):
    print(F"""{i}\t\t{d}"""		)





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		Tuple:







  """simple docstring"""





  for j in range(__a		):
    lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				str        =(graph[j][k] for k in ["src", "dst", "weight"])
    if distance[u] != float("inf"		) and distance[u] + w < distance[v]:
      return True
  return False





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a		)					->		list[float]:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =[float("inf"		)] * vertex_count
  lowerCamelCase__:				List[str]        =0.0

  for _ in range(vertex_count - 1		):
    for j in range(__a		):
      lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =(graph[j][k] for k in ["src", "dst", "weight"])

      if distance[u] != float("inf"		) and distance[u] + w < distance[v]:
        lowerCamelCase__:				int        =distance[u] + w

  lowerCamelCase__:				Tuple        =check_negative_cycle(__a     ,   __a     ,   __a		)
  if negative_cycle_exists:
    raise Exception("Negative cycle found"		)

  return distance


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     __A     =    int(input("Enter number of vertices: ").strip())
     __A     =    int(input("Enter number of edges: ").strip())

     __A     =    [{} for _ in range(E)]

     for i in range(E):
          print("Edge ", i + 1)
          __A ,     __A ,     __A     =    (
              int(x)
              for x in input("Enter source, destination, weight: ").strip().split(" ")
          )
          __A     =    {"src": src, "dst": dest, "weight": weight}

     __A     =    int(input("\nEnter shortest path source:").strip())
     __A     =    bellman_ford(graph, V, E, source)
     print_distance(shortest_distance, 0)

