





import operator as op





def 	lowerCAmelCase_					(			__a						)	->						Tuple:







  """simple docstring"""





  lowerCamelCase__: Optional[Any]         =[]
  lowerCamelCase__: Tuple         =lambda __a      ,       __a						: int(x / y						)  # noqa: E731 integer division operation
  lowerCamelCase__: Tuple         ={
      "^": op.pow,
      "*": op.mul,
      "/": div,
      "+": op.add,
      "-": op.sub,
  }  # operators & their respective operation

  # print table header
  print("Symbol".center(8						)      ,       "Action".center(12						)      ,       "Stack"      ,       sep=" | "						)
  print("-" * (30 + len(__a						))						)

  for x in post_fix:
    if x.isdigit():  # if x in digit
      stack.append(__a						)  # append x to stack
      # output in tabular format
      print(x.rjust(8						)      ,       ("push(" + x + ")").ljust(12						)      ,       ",".join(__a						)      ,       sep=" | "						)
    else:
      lowerCamelCase__: List[Any]         =stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8						)      ,       ("pop(" + b + ")").ljust(12						)      ,       ",".join(__a						)      ,       sep=" | "						)

      lowerCamelCase__: Optional[Any]         =stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8						)      ,       ("pop(" + a + ")").ljust(12						)      ,       ",".join(__a						)      ,       sep=" | "						)

      stack.append(
          str(opr[x](int(__a						)      ,       int(__a						)						)						)						)  # evaluate the 2 values popped from stack & push result to stack
      # output in tabular format
      print(
          x.rjust(8						)      ,       ("push(" + a + x + b + ")").ljust(12						)      ,       ",".join(__a						)      ,       sep=" | "      ,       )

  return int(stack[0]						)


if __name__ == "__main__":
     __A							   =							input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
     print("\n\tResult = ", solve(Postfix))





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

_lowerCAmelCase:						Tuple   				=       torch.device('cpu')






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






def   _lowercase(					__a				:						Optional[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(					__a				:						int    ,     __a				:						int    ,     __a				:						Optional[Any]     ):
							a__      =dct.pop(__a     )
							a__      =val






def   _lowercase(					__a				:						Optional[Any]     ):
							a__      =[]
							for k in state_dict.keys():
														a__      =k
														if ".pwconv" in k:
																					a__      =k_new.replace('.pwconv'    ,     '.point_wise_conv'     )
														if ".dwconv" in k:
																					a__      =k_new.replace('.dwconv'    ,     '.depth_wise_conv'     )
														if ".Proj." in k:
																					a__      =k_new.replace('.Proj.'    ,     '.proj.'     )
														if "patch_embed" in k_new:
																					a__      =k_new.replace('patch_embed'    ,     'swiftformer.patch_embed.patch_embedding'     )
														if "network" in k_new:
																					a__      =k_new.split('.'     )
																					if ls[2].isdigit():
																												a__      ='swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]     )
																					else:
																												a__      =k_new.replace('network'    ,     'swiftformer.encoder.network'     )
														rename_keys.append((k, k_new)     )
							return rename_keys






@torch.no_grad()
def   _lowercase(					__a				:						Union[str, Any]    ,     __a				:						int    ,     __a				:						str     ):
							a__      =SwiftFormerConfig()

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

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

							elif swiftformer_name == "swiftformer_s":
														a__      =[3, 3, 9, 6]
														a__      =[48, 64, 168, 224]

							elif swiftformer_name == "swiftformer_l1":
														a__      =[4, 3, 10, 5]
														a__      =[48, 96, 192, 384]

							elif swiftformer_name == "swiftformer_l3":
														a__      =[4, 4, 12, 6]
														a__      =[64, 128, 320, 512]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith('https'     ):
																					a__      =torch.hub.load_state_dict_from_url(__a    ,     map_location='cpu'    ,     check_hash=__a     )
														else:
																					a__      =torch.load(__a    ,     map_location='cpu'     )
							a__      =checkpoint

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

							# load HuggingFace model
							a__      =SwiftFormerForImageClassification(__a     ).eval()
							hf_model.load_state_dict(__a     )

							# prepare test inputs
							a__      =prepare_img()
							a__      =ViTImageProcessor.from_pretrained('preprocessor_config'     )
							a__      =processor(images=__a    ,     return_tensors='pt'     )

							# compare outputs from both models
							a__      =get_expected_output(__a     )
							a__      =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__":
	_lowerCAmelCase:						Optional[Any]   				=       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.')

	_lowerCAmelCase:						Optional[int]   				=       parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

