




"""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()
a         =      logging.get_logger(__name__)






def 						_snake_case					( _snake_case							: Tuple    ,  _snake_case							: Tuple    ,  _snake_case							: int    ,  _snake_case							: List[str]							)   -> Any:






      '''simple docstring'''


      _A        =       original_name.split('.'							)[0]
      _A        =       key.split('.'							)
      _A        =       int(key_list[key_list.index(_snake_case							) - 2]							)
      _A        =       int(key_list[key_list.index(_snake_case							) - 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 						_snake_case					( _snake_case							: Optional[Any]							)   -> Optional[Any]:






      '''simple docstring'''


      _A        =       OrderedDict()
      _A       ,   _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(_snake_case    ,  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(_snake_case    ,  _snake_case    ,  'mlp.fc1'    ,  'output.conv1'							)
            if "mlp.fc2" in key:
                  _A        =       replace_key_with_offset(_snake_case    ,  _snake_case    ,  'mlp.fc2'    ,  'output.conv2'							)
            if "norm1" in key:
                  _A        =       replace_key_with_offset(_snake_case    ,  _snake_case    ,  'norm1'    ,  'before_norm'							)
            if "norm2" in key:
                  _A        =       replace_key_with_offset(_snake_case    ,  _snake_case    ,  'norm2'    ,  'after_norm'							)
            if "layer_scale_1" in key:
                  _A        =       replace_key_with_offset(_snake_case    ,  _snake_case    ,  'layer_scale_1'    ,  'layer_scale_1'							)
            if "layer_scale_2" in key:
                  _A        =       replace_key_with_offset(_snake_case    ,  _snake_case    ,  'layer_scale_2'    ,  'layer_scale_2'							)
            if "head" in key:
                  _A        =       key.replace('head'    ,  'classifier'							)
            _A        =       value
      return new_state_dict






def 						_snake_case					( )   -> int:






      '''simple docstring'''


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

      return image






@torch.no_grad()
def 						_snake_case					( _snake_case							: Optional[Any]    ,  _snake_case							: Dict    ,  _snake_case							: Any							)   -> Union[str, Any]:






      '''simple docstring'''


      _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(_snake_case    ,  _snake_case    ,  repo_type='dataset'							)    ,  'r'							)							)
      _A        =       {int(_snake_case							): 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=_snake_case							)

      # Prepare image
      _A        =       prepare_img()
      _A        =       image_processor(images=_snake_case    ,  return_tensors='pt'							).pixel_values

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

      # load original state dict
      _A        =       torch.load(_snake_case    ,  map_location=torch.device('cpu'							)							)

      # rename keys
      _A        =       rename_keys(_snake_case							)

      # create HuggingFace model and load state dict
      _A        =       PoolFormerForImageClassification(_snake_case							)
      model.load_state_dict(_snake_case							)
      model.eval()

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

      # forward pass
      _A        =       model(_snake_case							)
      _A        =       outputs.logits

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







"""simple docstring"""

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


a         =      HUGGINGFACE_HUB_CACHE


a         =      '''config.json'''
a         =      '''diffusion_pytorch_model.bin'''
a         =      '''diffusion_flax_model.msgpack'''
a         =      '''model.onnx'''
a         =      '''diffusion_pytorch_model.safetensors'''
a         =      '''weights.pb'''
a         =      '''https://huggingface.co'''
a         =      default_cache_path
a         =      '''diffusers_modules'''
a         =      os.getenv('''HF_MODULES_CACHE''', os.path.join(hf_cache_home, '''modules'''))
a         =      ['''fp16''', '''non-ema''']
a         =      '''.self_attn'''

