


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()
__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

def 			_a  (				SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Optional[Any]    , SCREAMING_SNAKE_CASE	:				Union[str, Any]     ):







 """simple docstring"""



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

 UpperCamelCase__					:    Optional[Any]          =   key.replace(F"{orig_block_num}.{layer_num}.{original_name}"    , F"block.{new_block_num}.{layer_num}.{new_name}"     )
 return key

def 			_a  (				SCREAMING_SNAKE_CASE	:				Tuple     ):







 """simple docstring"""



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

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 UpperCamelCase__					:    List[Any]          =   Image.open(requests.get(SCREAMING_SNAKE_CASE    , stream=SCREAMING_SNAKE_CASE     ).raw     )

 return image

@torch.no_grad()
def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[Any]          =   PoolFormerConfig()

 # set attributes based on model_name
 UpperCamelCase__					:    Optional[Any]          =   '''huggingface/label-files'''
 UpperCamelCase__					:    Dict          =   model_name[-3:]
 UpperCamelCase__					:    Optional[int]          =   1000
 UpperCamelCase__					:    int          =   '''imagenet-1k-id2label.json'''
 UpperCamelCase__					:    int          =   (1, 1000)

 # set config attributes
 UpperCamelCase__					:    List[str]          =   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , repo_type='''dataset'''     )    , '''r'''     )     )
 UpperCamelCase__					:    str          =   {int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
 UpperCamelCase__					:    int          =   idalabel
 UpperCamelCase__					:    int          =   {v: k for k, v in idalabel.items()}
 if size == "s12":
  UpperCamelCase__					:    Union[str, Any]          =   [2, 2, 6, 2]
  UpperCamelCase__					:    Optional[int]          =   [64, 128, 320, 512]
  UpperCamelCase__					:    Optional[int]          =   4.0
  UpperCamelCase__					:    List[str]          =   0.9
 elif size == "s24":
  UpperCamelCase__					:    Union[str, Any]          =   [4, 4, 12, 4]
  UpperCamelCase__					:    str          =   [64, 128, 320, 512]
  UpperCamelCase__					:    Union[str, Any]          =   4.0
  UpperCamelCase__					:    Union[str, Any]          =   0.9
 elif size == "s36":
  UpperCamelCase__					:    int          =   [6, 6, 18, 6]
  UpperCamelCase__					:    Dict          =   [64, 128, 320, 512]
  UpperCamelCase__					:    List[Any]          =   4.0
  UpperCamelCase__					:    Tuple          =   1E-6
  UpperCamelCase__					:    Optional[int]          =   0.9
 elif size == "m36":
  UpperCamelCase__					:    int          =   [6, 6, 18, 6]
  UpperCamelCase__					:    Optional[Any]          =   [96, 192, 384, 768]
  UpperCamelCase__					:    str          =   4.0
  UpperCamelCase__					:    str          =   1E-6
  UpperCamelCase__					:    List[str]          =   0.95
 elif size == "m48":
  UpperCamelCase__					:    Union[str, Any]          =   [8, 8, 24, 8]
  UpperCamelCase__					:    List[Any]          =   [96, 192, 384, 768]
  UpperCamelCase__					:    str          =   4.0
  UpperCamelCase__					:    Any          =   1E-6
  UpperCamelCase__					:    List[str]          =   0.95
 else:
  raise ValueError(F"Size {size} not supported"     )

 # load image processor
 UpperCamelCase__					:    str          =   PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE     )

 # Prepare image
 UpperCamelCase__					:    Union[str, Any]          =   prepare_img()
 UpperCamelCase__					:    Union[str, Any]          =   image_processor(images=SCREAMING_SNAKE_CASE    , return_tensors='''pt'''     ).pixel_values

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

 # load original state dict
 UpperCamelCase__					:    Optional[Any]          =   torch.load(SCREAMING_SNAKE_CASE    , map_location=torch.device('''cpu'''     )     )

 # rename keys
 UpperCamelCase__					:    Optional[int]          =   rename_keys(SCREAMING_SNAKE_CASE     )

 # create HuggingFace model and load state dict
 UpperCamelCase__					:    List[Any]          =   PoolFormerForImageClassification(SCREAMING_SNAKE_CASE     )
 model.load_state_dict(SCREAMING_SNAKE_CASE     )
 model.eval()

 # Define image processor
 UpperCamelCase__					:    Optional[Any]          =   PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    int          =   image_processor(images=prepare_img()    , return_tensors='''pt'''     ).pixel_values

 # forward pass
 UpperCamelCase__					:    Optional[Any]          =   model(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Union[str, Any]          =   outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  UpperCamelCase__					:    str          =   torch.tensor([-0.3045, -0.6758, -0.4869]     )
 elif size == "s24":
  UpperCamelCase__					:    Tuple          =   torch.tensor([0.4402, -0.1374, -0.8045]     )
 elif size == "s36":
  UpperCamelCase__					:    List[Any]          =   torch.tensor([-0.6080, -0.5133, -0.5898]     )
 elif size == "m36":
  UpperCamelCase__					:    List[str]          =   torch.tensor([0.3952, 0.2263, -1.2668]     )
 elif size == "m48":
  UpperCamelCase__					:    Dict          =   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]    , 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__":
       __UpperCamelCase       :       Tuple						       =				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."
       )
       __UpperCamelCase       :       Optional[Any]						       =				parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






import math
import os
import sys

def 			_a  (				SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   ''''''
 try:
  with open(SCREAMING_SNAKE_CASE    , '''rb'''     ) as binary_file:
   UpperCamelCase__					:    int          =   binary_file.read()
  for dat in data:
   UpperCamelCase__					:    Dict          =   F"{dat:08b}"
   result += curr_byte
  return result
 except OSError:
  print('''File not accessible'''     )
  sys.exit()

def 			_a  (				SCREAMING_SNAKE_CASE	:				dict[str, str]    , SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 lexicon.pop(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    str          =   last_match_id

 if math.loga(SCREAMING_SNAKE_CASE     ).is_integer():
  for curr_key in lexicon:
   UpperCamelCase__					:    Any          =   '''0''' + lexicon[curr_key]

 UpperCamelCase__					:    Dict          =   bin(SCREAMING_SNAKE_CASE     )[2:]

def 			_a  (				SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   {'''0''': '''0''', '''1''': '''1'''}
 UpperCamelCase__       ,				UpperCamelCase__					:    Any          =   '''''', ''''''
 UpperCamelCase__					:    List[Any]          =   len(SCREAMING_SNAKE_CASE     )

 for i in range(len(SCREAMING_SNAKE_CASE     )     ):
  curr_string += data_bits[i]
  if curr_string not in lexicon:
   continue

  UpperCamelCase__					:    List[str]          =   lexicon[curr_string]
  result += last_match_id
  add_key_to_lexicon(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
  index += 1
  UpperCamelCase__					:    Dict          =   ''''''

 while curr_string != "" and curr_string not in lexicon:
  curr_string += "0"

 if curr_string != "":
  UpperCamelCase__					:    Union[str, Any]          =   lexicon[curr_string]
  result += last_match_id

 return result

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    int          =   os.path.getsize(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   bin(SCREAMING_SNAKE_CASE     )[2:]
 UpperCamelCase__					:    Tuple          =   len(SCREAMING_SNAKE_CASE     )

 return "0" * (length_length - 1) + file_length_binary + compressed

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   8
 try:
  with open(SCREAMING_SNAKE_CASE    , '''wb'''     ) as opened_file:
   UpperCamelCase__					:    Optional[Any]          =   [
       to_write[i : i + byte_length]
       for i in range(0    , len(SCREAMING_SNAKE_CASE     )    , SCREAMING_SNAKE_CASE     )
   ]

   if len(result_byte_array[-1]     ) % byte_length == 0:
    result_byte_array.append('''10000000'''     )
   else:
    result_byte_array[-1] += "1" + "0" * (
        byte_length - len(result_byte_array[-1]     ) - 1
    )

   for elem in result_byte_array:
    opened_file.write(int(SCREAMING_SNAKE_CASE    , 2     ).to_bytes(1    , byteorder='''big'''     )     )
 except OSError:
  print('''File not accessible'''     )
  sys.exit()

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[Any]          =   read_file_binary(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   compress_data(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   add_file_length(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 write_file_binary(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       compress(sys.argv[1], sys.argv[2])


