


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    :						Tuple     =							logging.get_logger(__name__)





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      str       						=						original_name.split("""."""      )[0]
      __lowerCamelCase     :      List[str]       						=						key.split("""."""      )
      __lowerCamelCase     :      Optional[int]       						=						int(key_list[key_list.index(UpperCAmelCase      ) - 2]      )
      __lowerCamelCase     :      int       						=						int(key_list[key_list.index(UpperCAmelCase      ) - 1]      )
      __lowerCamelCase     :      str       						=						orig_block_num - offset

      __lowerCamelCase     :      Optional[Any]       						=						key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""						,	f"""block.{new_block_num}.{layer_num}.{new_name}"""      )
      return key





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any      ):
      """simple docstring"""




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





def   _UpperCAmelCase       (     ):
      """simple docstring"""




      __lowerCamelCase     :      Dict       						=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      __lowerCamelCase     :      Tuple       						=						Image.open(requests.get(UpperCAmelCase						,	stream=UpperCAmelCase      ).raw      )

      return image





@torch.no_grad()
def   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			Dict      ):
      """simple docstring"""




      __lowerCamelCase     :      str       						=						PoolFormerConfig()

      # set attributes based on model_name
      __lowerCamelCase     :      Dict       						=						"""huggingface/label-files"""
      __lowerCamelCase     :      List[Any]       						=						model_name[-3:]
      __lowerCamelCase     :      List[Any]       						=						1_000
      __lowerCamelCase     :      List[str]       						=						"""imagenet-1k-id2label.json"""
      __lowerCamelCase     :      Tuple       						=						(1, 1_000)

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

      # load image processor
      __lowerCamelCase     :      str       						=						PoolFormerImageProcessor(crop_pct=UpperCAmelCase      )

      # Prepare image
      __lowerCamelCase     :      Union[str, Any]       						=						prepare_img()
      __lowerCamelCase     :      Any       						=						image_processor(images=UpperCAmelCase						,	return_tensors="""pt"""      ).pixel_values

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

      # load original state dict
      __lowerCamelCase     :      Dict       						=						torch.load(UpperCAmelCase						,	map_location=torch.device("""cpu"""      )      )

      # rename keys
      __lowerCamelCase     :      Optional[Any]       						=						rename_keys(UpperCAmelCase      )

      # create HuggingFace model and load state dict
      __lowerCamelCase     :      Optional[int]       						=						PoolFormerForImageClassification(UpperCAmelCase      )
      model.load_state_dict(UpperCAmelCase      )
      model.eval()

      # Define image processor
      __lowerCamelCase     :      str       						=						PoolFormerImageProcessor(crop_pct=UpperCAmelCase      )
      __lowerCamelCase     :      int       						=						image_processor(images=prepare_img()						,	return_tensors="""pt"""      ).pixel_values

      # forward pass
      __lowerCamelCase     :      Any       						=						model(UpperCAmelCase      )
      __lowerCamelCase     :      List[Any]       						=						outputs.logits

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







import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   def __init__(  self  : str       ,  _lowerCamelCase  : str = "▁"       ,  _lowerCamelCase  : bool = True       ,  _lowerCamelCase  : Union[str, AddedToken] = "<unk>"       ,  _lowerCamelCase  : Union[str, AddedToken] = "</s>"       ,  _lowerCamelCase  : Union[str, AddedToken] = "<pad>"       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						{
             """pad""": {"""id""": 0, """token""": pad_token},
             """eos""": {"""id""": 1, """token""": eos_token},
             """unk""": {"""id""": 2, """token""": unk_token},
         }

         __lowerCamelCase     :      Optional[Any]       						=						[None] * len(self.special_tokens							)
         for token_dict in self.special_tokens.values():
               __lowerCamelCase     :      Tuple       						=						token_dict["""token"""]

         __lowerCamelCase     :      Optional[int]       						=						Tokenizer(Unigram()							)

         __lowerCamelCase     :      str       						=						normalizers.Sequence(
             [
                 normalizers.Nmt(),
                 normalizers.NFKC(),
                 normalizers.Replace(Regex(""" {2,}"""							)       ,  """ """							),
                 normalizers.Lowercase(),
             ]							)
         __lowerCamelCase     :      int       						=						pre_tokenizers.Sequence(
             [
                 pre_tokenizers.Metaspace(replacement=_lowerCamelCase       ,  add_prefix_space=_lowerCamelCase							),
                 pre_tokenizers.Digits(individual_digits=_lowerCamelCase							),
                 pre_tokenizers.Punctuation(),
             ]							)
         __lowerCamelCase     :      Tuple       						=						decoders.Metaspace(replacement=_lowerCamelCase       ,  add_prefix_space=_lowerCamelCase							)

         __lowerCamelCase     :      Tuple       						=						TemplateProcessing(
             single=F"""$A {self.special_tokens["eos"]["token"]}"""       ,  special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])]       ,  )

         __lowerCamelCase     :      Tuple       						=						{
             """model""": """SentencePieceUnigram""",
             """replacement""": replacement,
             """add_prefix_space""": add_prefix_space,
         }

         super().__init__(_lowerCamelCase       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : Dict       ,  _lowerCamelCase  : Union[str, List[str]]       ,  _lowerCamelCase  : int = 8_0_0_0       ,  _lowerCamelCase  : bool = True       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						trainers.UnigramTrainer(
             vocab_size=_lowerCamelCase       ,  special_tokens=self.special_tokens_list       ,  show_progress=_lowerCamelCase       ,  )

         if isinstance(_lowerCamelCase       ,  _lowerCamelCase							):
               __lowerCamelCase     :      Optional[int]       						=						[files]
         self._tokenizer.train(_lowerCamelCase       ,  trainer=_lowerCamelCase							)

         self.add_unk_id()

   def   _snake_case    (  self  : List[Any]       ,  _lowerCamelCase  : Union[Iterator[str], Iterator[Iterator[str]]]       ,  _lowerCamelCase  : int = 8_0_0_0       ,  _lowerCamelCase  : bool = True       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						trainers.UnigramTrainer(
             vocab_size=_lowerCamelCase       ,  special_tokens=self.special_tokens_list       ,  show_progress=_lowerCamelCase       ,  )

         self._tokenizer.train_from_iterator(_lowerCamelCase       ,  trainer=_lowerCamelCase							)

         self.add_unk_id()


   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						json.loads(self._tokenizer.to_str()							)

         __lowerCamelCase     :      Optional[int]       						=						self.special_tokens["""unk"""]["""id"""]

         __lowerCamelCase     :      List[str]       						=						Tokenizer.from_str(json.dumps(_lowerCamelCase							)							)



