




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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Optional[int]:
     a     =				original_name.split(".")[0]
     a     =				key.split(".")
     a     =				int(key_list[key_list.index(UpperCAmelCase_) - 2])
     a     =				int(key_list[key_list.index(UpperCAmelCase_) - 1])
     a     =				orig_block_num - offset

     a     =				key.replace(f'''{orig_block_num}.{layer_num}.{original_name}'''		, f'''block.{new_block_num}.{layer_num}.{new_name}''')
     return key

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

def  SCREAMING_SNAKE_CASE							(       )      ->					List[str]:
     a     =				'http://images.cocodataset.org/val2017/000000039769.jpg'
     a     =				Image.open(requests.get(UpperCAmelCase_		, stream=UpperCAmelCase_).raw)

     return image

@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					str:
     a     =				PoolFormerConfig()

     # set attributes based on model_name
     a     =				'huggingface/label-files'
     a     =				model_name[-3:]
     a     =				10_00
     a     =				'imagenet-1k-id2label.json'
     a     =				(1, 10_00)

     # set config attributes
     a     =				json.load(open(hf_hub_download(UpperCAmelCase_		, UpperCAmelCase_		, repo_type="dataset")		, "r"))
     a     =				{int(UpperCAmelCase_): v for k, v in idalabel.items()}
     a     =				idalabel
     a     =				{v: k for k, v in idalabel.items()}
     if size == "s12":
          a     =				[2, 2, 6, 2]
          a     =				[64, 1_28, 3_20, 5_12]
          a     =				4.0
          a     =				0.9
     elif size == "s24":
          a     =				[4, 4, 12, 4]
          a     =				[64, 1_28, 3_20, 5_12]
          a     =				4.0
          a     =				0.9
     elif size == "s36":
          a     =				[6, 6, 18, 6]
          a     =				[64, 1_28, 3_20, 5_12]
          a     =				4.0
          a     =				1e-6
          a     =				0.9
     elif size == "m36":
          a     =				[6, 6, 18, 6]
          a     =				[96, 1_92, 3_84, 7_68]
          a     =				4.0
          a     =				1e-6
          a     =				0.95
     elif size == "m48":
          a     =				[8, 8, 24, 8]
          a     =				[96, 1_92, 3_84, 7_68]
          a     =				4.0
          a     =				1e-6
          a     =				0.95
     else:
          raise ValueError(f'''Size {size} not supported''')

     # load image processor
     a     =				PoolFormerImageProcessor(crop_pct=UpperCAmelCase_)

     # Prepare image
     a     =				prepare_img()
     a     =				image_processor(images=UpperCAmelCase_		, return_tensors="pt").pixel_values

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

     # load original state dict
     a     =				torch.load(UpperCAmelCase_		, map_location=torch.device("cpu"))

     # rename keys
     a     =				rename_keys(UpperCAmelCase_)

     # create HuggingFace model and load state dict
     a     =				PoolFormerForImageClassification(UpperCAmelCase_)
     model.load_state_dict(UpperCAmelCase_)
     model.eval()

     # Define image processor
     a     =				PoolFormerImageProcessor(crop_pct=UpperCAmelCase_)
     a     =				image_processor(images=prepare_img()		, return_tensors="pt").pixel_values

     # forward pass
     a     =				model(UpperCAmelCase_)
     a     =				outputs.logits

     # define expected logit slices for different models
     if size == "s12":
          a     =				torch.tensor([-0.3_045, -0.6_758, -0.4_869])
     elif size == "s24":
          a     =				torch.tensor([0.4_402, -0.1_374, -0.8_045])
     elif size == "s36":
          a     =				torch.tensor([-0.6_080, -0.5_133, -0.5_898])
     elif size == "m36":
          a     =				torch.tensor([0.3_952, 0.2_263, -1.2_668])
     elif size == "m48":
          a     =				torch.tensor([0.1_167, -0.0_656, -0.3_423])
     else:
          raise ValueError(f'''Size {size} not supported''')

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

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


if __name__ == "__main__":
   lowercase__				:      List[str]	   = 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."
   )
   lowercase__				:      str	   = parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







lowercase__				:      List[Any]	   = {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


lowercase__				:      str	   = {value: key for key, value in encode_dict.items()}

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     a     =				""
     for letter in word.lower():
          if letter.isalpha() or letter == " ":
               encoded += encode_dict[letter]
          else:
               raise Exception("encode() accepts only letters of the alphabet and spaces")
     return encoded

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     if set(__UpperCamelCase) - {"A", "B", " "} != set():
          raise Exception("decode() accepts only 'A', 'B' and spaces")
     a     =				""
     for word in coded.split():
          while len(__UpperCamelCase) != 0:
               decoded += decode_dict[word[:5]]
               a     =				word[5:]
          decoded += " "
     return decoded.strip()


if __name__ == "__main__":
   from doctest import testmod

   testmod()

