'''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()
SCREAMING_SNAKE_CASE_:					Optional[int]  =logging.get_logger(__name__)





def      lowerCAmelCase_    ( snake_case_	:			Tuple ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			Any ,   snake_case_	:			Any      )   ->				Tuple:


     '''simple docstring'''




     UpperCAmelCase_	   =       original_name.split("."      )[0]
     UpperCAmelCase_	   =       key.split("."      )
     UpperCAmelCase_	   =       int(key_list[key_list.index(snake_case_      ) - 2]      )
     UpperCAmelCase_	   =       int(key_list[key_list.index(snake_case_      ) - 1]      )
     UpperCAmelCase_	   =       orig_block_num - offset

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





def      lowerCAmelCase_    ( snake_case_	:			str      )   ->				str:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_	   =       "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_	   =       Image.open(requests.get(snake_case_ ,   stream=snake_case_      ).raw      )

     return image





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


     '''simple docstring'''




     UpperCAmelCase_	   =       PoolFormerConfig()

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

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

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

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

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

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

     # rename keys
     UpperCAmelCase_	   =       rename_keys(snake_case_      )

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

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

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

     # define expected logit slices for different models
     if size == "s12":
          UpperCAmelCase_	   =       torch.tensor([-0.3045, -0.6758, -0.4869]      )
     elif size == "s24":
          UpperCAmelCase_	   =       torch.tensor([0.4402, -0.1374, -0.8045]      )
     elif size == "s36":
          UpperCAmelCase_	   =       torch.tensor([-0.6080, -0.5133, -0.5898]      )
     elif size == "m36":
          UpperCAmelCase_	   =       torch.tensor([0.3952, 0.2263, -1.2668]      )
     elif size == "m48":
          UpperCAmelCase_	   =       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] ,   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(snake_case_      ).mkdir(exist_ok=snake_case_      )
     model.save_pretrained(snake_case_      )
     print(f"""Saving image processor to {pytorch_dump_folder_path}"""      )
     image_processor.save_pretrained(snake_case_      )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Optional[int]  =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.'
    )
    SCREAMING_SNAKE_CASE_:					Optional[int]  =parser.parse_args()
    convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''





def      lowerCAmelCase_    ( snake_case_	:			int = 1_00_00_00      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       limit + 1
     UpperCAmelCase_	   =       [0] * limit
     for first_term in range(1 ,   snake_case_      ):
          for n in range(snake_case_ ,   snake_case_ ,   snake_case_      ):
               UpperCAmelCase_	   =       first_term + n / first_term
               if common_difference % 4:  # d must be divisble by 4
                    continue
               else:
                    common_difference /= 4
                    if (
                        first_term > common_difference
                        and first_term < 4 * common_difference
                    ):  # since x,y,z are positive integers
                         frequency[n] += 1  # so z>0 and a>d ,also 4d<a

     UpperCAmelCase_	   =       sum(1 for x in frequency[1:limit] if x == 10      )

     return count


if __name__ == "__main__":
    print(f"{solution() = }")




