





"""simple docstring"""




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A     =    logging.get_logger(__name__)





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







  """simple docstring"""





  lowerCamelCase__:				Dict        =original_name.split("."		)[0]
  lowerCamelCase__:				Any        =key.split("."		)
  lowerCamelCase__:				Union[str, Any]        =int(key_list[key_list.index(__SCREAMING_SNAKE_CASE		) - 2]		)
  lowerCamelCase__:				List[Any]        =int(key_list[key_list.index(__SCREAMING_SNAKE_CASE		) - 1]		)
  lowerCamelCase__:				List[str]        =orig_block_num - offset

  lowerCamelCase__:				Tuple        =key.replace(F"""{orig_block_num}.{layer_num}.{original_name}"""     ,   F"""block.{new_block_num}.{layer_num}.{new_name}"""		)
  return key





def 					lowerCAmelCase_			(	__a		)					->		int:







  """simple docstring"""





  lowerCamelCase__:				List[Any]        =OrderedDict()
  lowerCamelCase__:				Optional[int]        =0, 0
  for key, value in state_dict.items():
    if key.startswith("network"		):
      lowerCamelCase__:				Dict        =key.replace("network"     ,   "poolformer.encoder"		)
    if "proj" in key:
      # Works for the first embedding as well as the internal embedding layers
      if key.endswith("bias"		) and "patch_embed" not in key:
        patch_emb_offset += 1
      lowerCamelCase__:				int        =key[: key.find("proj"		)]
      lowerCamelCase__:				Dict        =key.replace(__SCREAMING_SNAKE_CASE     ,   F"""patch_embeddings.{total_embed_found}."""		)
      lowerCamelCase__:				Optional[int]        =key.replace("proj"     ,   "projection"		)
      if key.endswith("bias"		):
        total_embed_found += 1
    if "patch_embeddings" in key:
      lowerCamelCase__:				int        ="poolformer.encoder." + key
    if "mlp.fc1" in key:
      lowerCamelCase__:				Optional[Any]        =replace_key_with_offset(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE     ,   "mlp.fc1"     ,   "output.conv1"		)
    if "mlp.fc2" in key:
      lowerCamelCase__:				Dict        =replace_key_with_offset(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE     ,   "mlp.fc2"     ,   "output.conv2"		)
    if "norm1" in key:
      lowerCamelCase__:				Dict        =replace_key_with_offset(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE     ,   "norm1"     ,   "before_norm"		)
    if "norm2" in key:
      lowerCamelCase__:				Dict        =replace_key_with_offset(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE     ,   "norm2"     ,   "after_norm"		)
    if "layer_scale_1" in key:
      lowerCamelCase__:				Optional[int]        =replace_key_with_offset(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE     ,   "layer_scale_1"     ,   "layer_scale_1"		)
    if "layer_scale_2" in key:
      lowerCamelCase__:				Any        =replace_key_with_offset(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE     ,   "layer_scale_2"     ,   "layer_scale_2"		)
    if "head" in key:
      lowerCamelCase__:				int        =key.replace("head"     ,   "classifier"		)
    lowerCamelCase__:				Tuple        =value
  return new_state_dict





def 					lowerCAmelCase_			(	)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        ="http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase__:				int        =Image.open(requests.get(__SCREAMING_SNAKE_CASE     ,   stream=__SCREAMING_SNAKE_CASE		).raw		)

  return image





@torch.no_grad()
def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:				Any        =PoolFormerConfig()

  # set attributes based on model_name
  lowerCamelCase__:				Any        ="huggingface/label-files"
  lowerCamelCase__:				int        =model_name[-3:]
  lowerCamelCase__:				List[Any]        =1000
  lowerCamelCase__:				Tuple        ="imagenet-1k-id2label.json"
  lowerCamelCase__:				str        =(1, 1000)

  # set config attributes
  lowerCamelCase__:				Dict        =json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE     ,   repo_type="dataset"		)     ,   "r"		)		)
  lowerCamelCase__:				List[str]        ={int(__SCREAMING_SNAKE_CASE		): v for k, v in idalabel.items()}
  lowerCamelCase__:				Any        =idalabel
  lowerCamelCase__:				Any        ={v: k for k, v in idalabel.items()}
  if size == "s12":
    lowerCamelCase__:				Dict        =[2, 2, 6, 2]
    lowerCamelCase__:				str        =[64, 128, 320, 512]
    lowerCamelCase__:				Optional[Any]        =4.0
    lowerCamelCase__:				Union[str, Any]        =0.9
  elif size == "s24":
    lowerCamelCase__:				Tuple        =[4, 4, 12, 4]
    lowerCamelCase__:				List[str]        =[64, 128, 320, 512]
    lowerCamelCase__:				Tuple        =4.0
    lowerCamelCase__:				Optional[int]        =0.9
  elif size == "s36":
    lowerCamelCase__:				int        =[6, 6, 18, 6]
    lowerCamelCase__:				int        =[64, 128, 320, 512]
    lowerCamelCase__:				List[str]        =4.0
    lowerCamelCase__:				Dict        =1e-6
    lowerCamelCase__:				List[Any]        =0.9
  elif size == "m36":
    lowerCamelCase__:				Dict        =[6, 6, 18, 6]
    lowerCamelCase__:				Dict        =[96, 192, 384, 768]
    lowerCamelCase__:				str        =4.0
    lowerCamelCase__:				Union[str, Any]        =1e-6
    lowerCamelCase__:				Union[str, Any]        =0.9_5
  elif size == "m48":
    lowerCamelCase__:				str        =[8, 8, 24, 8]
    lowerCamelCase__:				Optional[int]        =[96, 192, 384, 768]
    lowerCamelCase__:				str        =4.0
    lowerCamelCase__:				int        =1e-6
    lowerCamelCase__:				str        =0.9_5
  else:
    raise ValueError(F"""Size {size} not supported"""		)

  # load image processor
  lowerCamelCase__:				Union[str, Any]        =PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE		)

  # Prepare image
  lowerCamelCase__:				int        =prepare_img()
  lowerCamelCase__:				Tuple        =image_processor(images=__SCREAMING_SNAKE_CASE     ,   return_tensors="pt"		).pixel_values

  logger.info(F"""Converting model {model_name}..."""		)

  # load original state dict
  lowerCamelCase__:				Optional[int]        =torch.load(__SCREAMING_SNAKE_CASE     ,   map_location=torch.device("cpu"		)		)

  # rename keys
  lowerCamelCase__:				Any        =rename_keys(__SCREAMING_SNAKE_CASE		)

  # create HuggingFace model and load state dict
  lowerCamelCase__:				str        =PoolFormerForImageClassification(__SCREAMING_SNAKE_CASE		)
  model.load_state_dict(__SCREAMING_SNAKE_CASE		)
  model.eval()

  # Define image processor
  lowerCamelCase__:				Any        =PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE		)
  lowerCamelCase__:				Any        =image_processor(images=prepare_img()     ,   return_tensors="pt"		).pixel_values

  # forward pass
  lowerCamelCase__:				int        =model(__SCREAMING_SNAKE_CASE		)
  lowerCamelCase__:				Union[str, Any]        =outputs.logits

  # define expected logit slices for different models
  if size == "s12":
    lowerCamelCase__:				List[str]        =torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]		)
  elif size == "s24":
    lowerCamelCase__:				Optional[int]        =torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]		)
  elif size == "s36":
    lowerCamelCase__:				List[str]        =torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]		)
  elif size == "m36":
    lowerCamelCase__:				Union[str, Any]        =torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]		)
  elif size == "m48":
    lowerCamelCase__:				List[str]        =torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]		)
  else:
    raise ValueError(F"""Size {size} not supported"""		)

  # verify logits
  assert logits.shape == expected_shape
  assert torch.allclose(logits[0, :3]     ,   __SCREAMING_SNAKE_CASE     ,   atol=1e-2		)

  # finally, save model and image processor
  logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""		)
  Path(__SCREAMING_SNAKE_CASE		).mkdir(exist_ok=__SCREAMING_SNAKE_CASE		)
  model.save_pretrained(__SCREAMING_SNAKE_CASE		)
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""		)
  image_processor.save_pretrained(__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
     __A     =    argparse.ArgumentParser()

     parser.add_argument(
         "--model_name",
         default="poolformer_s12",
         type=str,
         help="Name of the model you'd like to convert.",
     )
     parser.add_argument(
         "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
     )
     __A     =    parser.parse_args()
     convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








from __future__ import annotations

from typing import Any



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       Any	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   lowerCamelCase__:				int        =num_of_nodes
   lowerCamelCase__:				list[list[int]]        =[]
   lowerCamelCase__:				dict[int, int]        ={}

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   self.m_edges.append([u_node, v_node, weight])

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       int)	->int:



   '''simple docstring'''



   if self.m_component[u_node] == u_node:
     return u_node
   return self.find_component(self.m_component[u_node])

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   if self.m_component[u_node] != u_node:
     for k in self.m_component:
       lowerCamelCase__:				Dict        =self.find_component(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       list[int]	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   if component_size[u_node] <= component_size[v_node]:
     lowerCamelCase__:				Optional[int]        =v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(UpperCAmelCase_)

   elif component_size[u_node] >= component_size[v_node]:
     lowerCamelCase__:				Tuple        =self.find_component(UpperCAmelCase_)
     component_size[u_node] += component_size[v_node]
     self.set_component(UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->None:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =[]
   lowerCamelCase__:				List[str]        =0

   lowerCamelCase__:				list[Any]        =[-1] * self.m_num_of_nodes

   # A list of components (initialized to all of the nodes)
   for node in range(self.m_num_of_nodes):
     self.m_component.update({node: node})
     component_size.append(1)

   lowerCamelCase__:				List[str]        =self.m_num_of_nodes

   while num_of_components > 1:
     for edge in self.m_edges:
       lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Tuple        =edge

       lowerCamelCase__:				List[Any]        =self.m_component[u]
       lowerCamelCase__:				str        =self.m_component[v]

       if u_component != v_component:

         for component in (u_component, v_component):
           if (
               minimum_weight_edge[component] == -1
               or minimum_weight_edge[component][2] > w
           ):
             lowerCamelCase__:				Union[str, Any]        =[u, v, w]

     for edge in minimum_weight_edge:
       if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
         lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Tuple        =edge

         lowerCamelCase__:				str        =self.m_component[u]
         lowerCamelCase__:				Any        =self.m_component[v]

         if u_component != v_component:
           mst_weight += w
           self.union(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_)
           print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n""")
           num_of_components -= 1

     lowerCamelCase__:				Tuple        =[-1] * self.m_num_of_nodes
   print(F"""The total weight of the minimal spanning tree is: {mst_weight}""")





def 					lowerCAmelCase_			(	)					->		None:







  """simple docstring"""







if __name__ == "__main__":
     import doctest

     doctest.testmod()

