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



def  __lowerCamelCase       (      _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  original_name.split('''.'''					)[0]
      UpperCAmelCase_            =  key.split('''.'''					)
      UpperCAmelCase_            =  int(key_list[key_list.index(_UpperCamelCase					) - 2]					)
      UpperCAmelCase_            =  int(key_list[key_list.index(_UpperCamelCase					) - 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       (      _UpperCamelCase  :  int					):


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



def  __lowerCamelCase       (      ):


      '''simple docstring'''


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

      return image



@torch.no_grad()
def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  PoolFormerConfig()

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

      # set config attributes
      UpperCAmelCase_            =  json.load(open(hf_hub_download(_UpperCamelCase							,    _UpperCamelCase							,    repo_type='''dataset'''					)							,    '''r'''					)					)
      UpperCAmelCase_            =  {int(_UpperCamelCase					): 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, 128, 320, 512]
            UpperCAmelCase_            =  4.0
            UpperCAmelCase_            =  0.9
      elif size == "s24":
            UpperCAmelCase_            =  [4, 4, 12, 4]
            UpperCAmelCase_            =  [64, 128, 320, 512]
            UpperCAmelCase_            =  4.0
            UpperCAmelCase_            =  0.9
      elif size == "s36":
            UpperCAmelCase_            =  [6, 6, 18, 6]
            UpperCAmelCase_            =  [64, 128, 320, 512]
            UpperCAmelCase_            =  4.0
            UpperCAmelCase_            =  1E-6
            UpperCAmelCase_            =  0.9
      elif size == "m36":
            UpperCAmelCase_            =  [6, 6, 18, 6]
            UpperCAmelCase_            =  [96, 192, 384, 768]
            UpperCAmelCase_            =  4.0
            UpperCAmelCase_            =  1E-6
            UpperCAmelCase_            =  0.95
      elif size == "m48":
            UpperCAmelCase_            =  [8, 8, 24, 8]
            UpperCAmelCase_            =  [96, 192, 384, 768]
            UpperCAmelCase_            =  4.0
            UpperCAmelCase_            =  1E-6
            UpperCAmelCase_            =  0.95
      else:
            raise ValueError(F"""Size {size} not supported"""					)

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

      # Prepare image
      UpperCAmelCase_            =  prepare_img()
      UpperCAmelCase_            =  image_processor(images=_UpperCamelCase							,    return_tensors='''pt'''					).pixel_values

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

      # load original state dict
      UpperCAmelCase_            =  torch.load(_UpperCamelCase							,    map_location=torch.device('''cpu'''					)					)

      # rename keys
      UpperCAmelCase_            =  rename_keys(_UpperCamelCase					)

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

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

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

      # define expected logit slices for different models
      if size == "s12":
            UpperCAmelCase_            =  torch.tensor([-0.3_045, -0.6_758, -0.4_869]					)
      elif size == "s24":
            UpperCAmelCase_            =  torch.tensor([0.4_402, -0.1_374, -0.8_045]					)
      elif size == "s36":
            UpperCAmelCase_            =  torch.tensor([-0.6_080, -0.5_133, -0.5_898]					)
      elif size == "m36":
            UpperCAmelCase_            =  torch.tensor([0.3_952, 0.2_263, -1.2_668]					)
      elif size == "m48":
            UpperCAmelCase_            =  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__  :      Any        =					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__  :      List[str]        =					parser.parse_args()
      convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''
import re



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]'''							,    str_					)]



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  split_input(str_					)
      return "".join(
          [''''''.join([char.capitalize() for char in sub_str]					) for sub_str in string_split]					)



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  bool							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      try:
            UpperCAmelCase_            =  split_input(_UpperCamelCase					)
            if upper:
                  UpperCAmelCase_            =  ''''''.join(
                      [
                          separator.join([char.upper() for char in sub_str]					)
                          for sub_str in string_split
                      ]					)
            else:
                  UpperCAmelCase_            =  ''''''.join(
                      [
                          separator.join([char.lower() for char in sub_str]					)
                          for sub_str in string_split
                      ]					)
            return res_str
      except IndexError:
            return "not valid string"



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      return to_simple_case(_UpperCamelCase					)



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      try:
            UpperCAmelCase_            =  to_simple_case(_UpperCamelCase					)
            return res_str[0].lower() + res_str[1:]
      except IndexError:
            return "not valid string"



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  bool					):


      '''simple docstring'''


      return to_complex_case(_UpperCamelCase							,    _UpperCamelCase							,    '''_'''					)



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  bool					):


      '''simple docstring'''


      return to_complex_case(_UpperCamelCase							,    _UpperCamelCase							,    '''-'''					)


if __name__ == "__main__":
      __import__("doctest").testmod()





