


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      =   logging.get_logger(__name__)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

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

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Optional[int]:




      """simple docstring"""

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






def 	__lowerCAmelCase   ()->  str:




      """simple docstring"""

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

      return image






@torch.no_grad()
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      snake_case_	=				PoolFormerConfig()

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

      # set config attributes
      snake_case_	=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , repo_type='''dataset''' ) , '''r''' ) )
      snake_case_	=				{int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
      snake_case_	=				idalabel
      snake_case_	=				{v: k for k, v in idalabel.items()}
      if size == "s12":
            snake_case_	=				[2, 2, 6, 2]
            snake_case_	=				[64, 128, 320, 512]
            snake_case_	=				4.0
            snake_case_	=				0.9
      elif size == "s24":
            snake_case_	=				[4, 4, 12, 4]
            snake_case_	=				[64, 128, 320, 512]
            snake_case_	=				4.0
            snake_case_	=				0.9
      elif size == "s36":
            snake_case_	=				[6, 6, 18, 6]
            snake_case_	=				[64, 128, 320, 512]
            snake_case_	=				4.0
            snake_case_	=				1E-6
            snake_case_	=				0.9
      elif size == "m36":
            snake_case_	=				[6, 6, 18, 6]
            snake_case_	=				[96, 192, 384, 768]
            snake_case_	=				4.0
            snake_case_	=				1E-6
            snake_case_	=				0.9_5
      elif size == "m48":
            snake_case_	=				[8, 8, 24, 8]
            snake_case_	=				[96, 192, 384, 768]
            snake_case_	=				4.0
            snake_case_	=				1E-6
            snake_case_	=				0.9_5
      else:
            raise ValueError(f'''Size {size} not supported''' )

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

      # Prepare image
      snake_case_	=				prepare_img()
      snake_case_	=				image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values

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

      # load original state dict
      snake_case_	=				torch.load(SCREAMING_SNAKE_CASE , map_location=torch.device('''cpu''' ) )

      # rename keys
      snake_case_	=				rename_keys(SCREAMING_SNAKE_CASE )

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

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

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

      # define expected logit slices for different models
      if size == "s12":
            snake_case_	=				torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9] )
      elif size == "s24":
            snake_case_	=				torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5] )
      elif size == "s36":
            snake_case_	=				torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8] )
      elif size == "m36":
            snake_case_	=				torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8] )
      elif size == "m48":
            snake_case_	=				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__":
   UpperCAmelCase      =   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      =   parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      return x if y == 0 else greatest_common_divisor(SCREAMING_SNAKE_CASE , x % y )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      return (x * y) // greatest_common_divisor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE = 20 )->  int:




      """simple docstring"""

      snake_case_	=				1
      for i in range(1 , n + 1 ):
            snake_case_	=				lcm(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      return g


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