
'''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()
__lowerCAmelCase         =    logging.get_logger(__name__)





def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    Any ,	__a      :    Optional[int] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Optional[Any]			=   original_name.split('.' )[0]
   _a				:	Union[str, Any]			=   key.split('.' )
   _a				:	Optional[int]			=   int(key_list[key_list.index(__a ) - 2] )
   _a				:	Optional[Any]			=   int(key_list[key_list.index(__a ) - 1] )
   _a				:	Dict			=   orig_block_num - offset

   _a				:	Any			=   key.replace(f"""{orig_block_num}.{layer_num}.{original_name}""" ,	f"""block.{new_block_num}.{layer_num}.{new_name}""" )
   return key





def 					UpperCAmelCase_		(__a      :    Optional[Any] ):






   """simple docstring"""

   _a				:	Any			=   OrderedDict()
   _a,  _a				:	str			=   0, 0
   for key, value in state_dict.items():
      if key.startswith('network' ):
         _a				:	List[Any]			=   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
         _a				:	str			=   key[: key.find('proj' )]
         _a				:	Optional[Any]			=   key.replace(__a ,	f"""patch_embeddings.{total_embed_found}.""" )
         _a				:	str			=   key.replace('proj' ,	'projection' )
         if key.endswith('bias' ):
            total_embed_found += 1
      if "patch_embeddings" in key:
         _a				:	Optional[int]			=   'poolformer.encoder.' + key
      if "mlp.fc1" in key:
         _a				:	List[Any]			=   replace_key_with_offset(__a ,	__a ,	'mlp.fc1' ,	'output.conv1' )
      if "mlp.fc2" in key:
         _a				:	Optional[Any]			=   replace_key_with_offset(__a ,	__a ,	'mlp.fc2' ,	'output.conv2' )
      if "norm1" in key:
         _a				:	List[str]			=   replace_key_with_offset(__a ,	__a ,	'norm1' ,	'before_norm' )
      if "norm2" in key:
         _a				:	Optional[Any]			=   replace_key_with_offset(__a ,	__a ,	'norm2' ,	'after_norm' )
      if "layer_scale_1" in key:
         _a				:	Optional[int]			=   replace_key_with_offset(__a ,	__a ,	'layer_scale_1' ,	'layer_scale_1' )
      if "layer_scale_2" in key:
         _a				:	Optional[Any]			=   replace_key_with_offset(__a ,	__a ,	'layer_scale_2' ,	'layer_scale_2' )
      if "head" in key:
         _a				:	str			=   key.replace('head' ,	'classifier' )
      _a				:	Optional[int]			=   value
   return new_state_dict





def 					UpperCAmelCase_		():






   """simple docstring"""

   _a				:	Dict			=   'http://images.cocodataset.org/val2017/000000039769.jpg'
   _a				:	str			=   Image.open(requests.get(__a ,	stream=__a ).raw )

   return image





@torch.no_grad()
def 					UpperCAmelCase_		(__a      :    str ,	__a      :    Union[str, Any] ,	__a      :    List[Any] ):






   """simple docstring"""

   _a				:	Tuple			=   PoolFormerConfig()

   # set attributes based on model_name
   _a				:	Optional[int]			=   'huggingface/label-files'
   _a				:	Dict			=   model_name[-3:]
   _a				:	Optional[int]			=   1_0_0_0
   _a				:	int			=   'imagenet-1k-id2label.json'
   _a				:	Optional[Any]			=   (1, 1_0_0_0)

   # set config attributes
   _a				:	Union[str, Any]			=   json.load(open(hf_hub_download(__a ,	__a ,	repo_type='dataset' ) ,	'r' ) )
   _a				:	Optional[int]			=   {int(__a ): v for k, v in idalabel.items()}
   _a				:	Union[str, Any]			=   idalabel
   _a				:	Dict			=   {v: k for k, v in idalabel.items()}
   if size == "s12":
      _a				:	Tuple			=   [2, 2, 6, 2]
      _a				:	Dict			=   [6_4, 1_2_8, 3_2_0, 5_1_2]
      _a				:	Any			=   4.0
      _a				:	List[str]			=   0.9
   elif size == "s24":
      _a				:	Optional[int]			=   [4, 4, 1_2, 4]
      _a				:	Any			=   [6_4, 1_2_8, 3_2_0, 5_1_2]
      _a				:	List[str]			=   4.0
      _a				:	Union[str, Any]			=   0.9
   elif size == "s36":
      _a				:	Any			=   [6, 6, 1_8, 6]
      _a				:	Tuple			=   [6_4, 1_2_8, 3_2_0, 5_1_2]
      _a				:	Dict			=   4.0
      _a				:	Dict			=   1e-6
      _a				:	Tuple			=   0.9
   elif size == "m36":
      _a				:	str			=   [6, 6, 1_8, 6]
      _a				:	Dict			=   [9_6, 1_9_2, 3_8_4, 7_6_8]
      _a				:	Union[str, Any]			=   4.0
      _a				:	List[str]			=   1e-6
      _a				:	Optional[Any]			=   0.95
   elif size == "m48":
      _a				:	Any			=   [8, 8, 2_4, 8]
      _a				:	List[Any]			=   [9_6, 1_9_2, 3_8_4, 7_6_8]
      _a				:	Optional[int]			=   4.0
      _a				:	List[Any]			=   1e-6
      _a				:	str			=   0.95
   else:
      raise ValueError(f"""Size {size} not supported""" )

   # load image processor
   _a				:	Optional[int]			=   PoolFormerImageProcessor(crop_pct=__a )

   # Prepare image
   _a				:	List[str]			=   prepare_img()
   _a				:	Optional[int]			=   image_processor(images=__a ,	return_tensors='pt' ).pixel_values

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

   # load original state dict
   _a				:	str			=   torch.load(__a ,	map_location=torch.device('cpu' ) )

   # rename keys
   _a				:	int			=   rename_keys(__a )

   # create HuggingFace model and load state dict
   _a				:	Tuple			=   PoolFormerForImageClassification(__a )
   model.load_state_dict(__a )
   model.eval()

   # Define image processor
   _a				:	Any			=   PoolFormerImageProcessor(crop_pct=__a )
   _a				:	int			=   image_processor(images=prepare_img() ,	return_tensors='pt' ).pixel_values

   # forward pass
   _a				:	Optional[int]			=   model(__a )
   _a				:	Union[str, Any]			=   outputs.logits

   # define expected logit slices for different models
   if size == "s12":
      _a				:	Union[str, Any]			=   torch.tensor([-0.3045, -0.6758, -0.4869] )
   elif size == "s24":
      _a				:	int			=   torch.tensor([0.4402, -0.1374, -0.8045] )
   elif size == "s36":
      _a				:	str			=   torch.tensor([-0.6080, -0.5133, -0.5898] )
   elif size == "m36":
      _a				:	List[str]			=   torch.tensor([0.3952, 0.2263, -1.2668] )
   elif size == "m48":
      _a				:	Any			=   torch.tensor([0.1167, -0.0656, -0.3423] )
   else:
      raise ValueError(f"""Size {size} not supported""" )

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

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


if __name__ == "__main__":
  __lowerCAmelCase         =    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."""
  )
  __lowerCAmelCase         =    parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''







from __future__ import annotations

from random import choice





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   return choice(__a )





def 					UpperCAmelCase_		(__a      :    list[int] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Dict			=   random_pivot(__a )

   # partition based on pivot
   # linear time
   _a				:	Optional[int]			=   [e for e in lst if e < pivot]
   _a				:	List[str]			=   [e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__a ) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__a ) < k - 1:
      return kth_number(__a ,	k - len(__a ) - 1 )
   # pivot is in elements smaller than k
   else:
      return kth_number(__a ,	__a )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



