




"""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  UpperCAmelCase__	(snake_case__							: Tuple		,       snake_case__							: Any		,       snake_case__							: Tuple		,       snake_case__							: List[str]   ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  original_name.split("""."""   )[0]
  _snake_case							:		List[str]								=  key.split("""."""   )
  _snake_case							:		List[Any]								=  int(key_list[key_list.index(snake_case__   ) - 2]   )
  _snake_case							:		Optional[int]								=  int(key_list[key_list.index(snake_case__   ) - 1]   )
  _snake_case							:		Union[str, Any]								=  orig_block_num - offset

  _snake_case							:		Union[str, Any]								=  key.replace(F"{orig_block_num}.{layer_num}.{original_name}"		,       F"block.{new_block_num}.{layer_num}.{new_name}"   )
  return key


def  UpperCAmelCase__	(snake_case__							: Dict   ):
  """simple docstring"""

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


def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		Dict								=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _snake_case							:		int								=  Image.open(requests.get(snake_case__		,       stream=snake_case__   ).raw   )

  return image


@torch.no_grad()
def  UpperCAmelCase__	(snake_case__							: str		,       snake_case__							: Optional[int]		,       snake_case__							: Union[str, Any]   ):
  """simple docstring"""

  _snake_case							:		int								=  PoolFormerConfig()

  # set attributes based on model_name
  _snake_case							:		Dict								=  """huggingface/label-files"""
  _snake_case							:		List[str]								=  model_name[-3:]
  _snake_case							:		List[str]								=  10_00
  _snake_case							:		Optional[int]								=  """imagenet-1k-id2label.json"""
  _snake_case							:		List[Any]								=  (1, 10_00)

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

  # load image processor
  _snake_case							:		Union[str, Any]								=  PoolFormerImageProcessor(crop_pct=snake_case__   )

  # Prepare image
  _snake_case							:		Optional[Any]								=  prepare_img()
  _snake_case							:		List[str]								=  image_processor(images=snake_case__		,       return_tensors="""pt"""   ).pixel_values

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

  # load original state dict
  _snake_case							:		Tuple								=  torch.load(snake_case__		,       map_location=torch.device("""cpu"""   )   )

  # rename keys
  _snake_case							:		Tuple								=  rename_keys(snake_case__   )

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

  # Define image processor
  _snake_case							:		str								=  PoolFormerImageProcessor(crop_pct=snake_case__   )
  _snake_case							:		Union[str, Any]								=  image_processor(images=prepare_img()		,       return_tensors="""pt"""   ).pixel_values

  # forward pass
  _snake_case							:		Optional[Any]								=  model(snake_case__   )
  _snake_case							:		int								=  outputs.logits

  # define expected logit slices for different models
  if size == "s12":
    _snake_case							:		Any								=  torch.tensor([-0.30_45, -0.67_58, -0.48_69]   )
  elif size == "s24":
    _snake_case							:		Union[str, Any]								=  torch.tensor([0.44_02, -0.13_74, -0.80_45]   )
  elif size == "s36":
    _snake_case							:		Union[str, Any]								=  torch.tensor([-0.60_80, -0.51_33, -0.58_98]   )
  elif size == "m36":
    _snake_case							:		str								=  torch.tensor([0.39_52, 0.22_63, -1.26_68]   )
  elif size == "m48":
    _snake_case							:		Optional[int]								=  torch.tensor([0.11_67, -0.06_56, -0.34_23]   )
  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"""


def  UpperCAmelCase__	(snake_case__							: Optional[Any]   ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  []
  _snake_case							:		Dict								=  set({"""(""", """[""", """{"""}   )
  _snake_case							:		Union[str, Any]								=  set({""")""", """]""", """}"""}   )
  _snake_case							:		Tuple								=  {"""{""": """}""", """[""": """]""", """(""": """)"""}

  for i in range(len(snake_case__   )   ):
    if s[i] in open_brackets:
      stack.append(s[i]   )

    elif s[i] in closed_brackets and (
        len(snake_case__   ) == 0 or (len(snake_case__   ) > 0 and open_to_closed[stack.pop()] != s[i])
    ):
      return False

  return len(snake_case__   ) == 0


def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		Any								=  input("""Enter sequence of brackets: """   )
  if is_balanced(snake_case__   ):
    print(snake_case__		,       """is balanced"""   )
  else:
    print(snake_case__		,       """is not balanced"""   )


if __name__ == "__main__":
     main()


