
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    :   str		       =				logging.get_logger(__name__)







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       Optional[Any]:
       lowercase				:	Optional[Any]			      = original_name.split("""."""							)[0]
       lowercase				:	Union[str, Any]			      = key.split("""."""							)
       lowercase				:	Optional[int]			      = int(key_list[key_list.index(_lowerCAmelCase							) - 2]							)
       lowercase				:	Optional[int]			      = int(key_list[key_list.index(_lowerCAmelCase							) - 1]							)
       lowercase				:	int			      = orig_block_num - offset

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







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







def 		_snake_case(    )					->       Any:
       lowercase				:	int			      = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase				:	List[str]			      = Image.open(requests.get(_lowerCAmelCase   ,      stream=_lowerCAmelCase							).raw							)

       return image







@torch.no_grad()
def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       Optional[Any]:
       lowercase				:	Optional[Any]			      = PoolFormerConfig()

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

       # set config attributes
       lowercase				:	Tuple			      = json.load(open(hf_hub_download(_lowerCAmelCase   ,      _lowerCAmelCase   ,      repo_type="""dataset"""							)   ,      """r"""							)							)
       lowercase				:	Any			      = {int(_lowerCAmelCase							): v for k, v in idalabel.items()}
       lowercase				:	List[Any]			      = idalabel
       lowercase				:	int			      = {v: k for k, v in idalabel.items()}
       if size == "s12":
              lowercase				:	Optional[Any]			      = [2, 2, 6, 2]
              lowercase				:	List[Any]			      = [64, 128, 320, 512]
              lowercase				:	List[Any]			      = 4.0
              lowercase				:	Optional[int]			      = 0.9
       elif size == "s24":
              lowercase				:	Tuple			      = [4, 4, 12, 4]
              lowercase				:	Tuple			      = [64, 128, 320, 512]
              lowercase				:	List[Any]			      = 4.0
              lowercase				:	Any			      = 0.9
       elif size == "s36":
              lowercase				:	Optional[Any]			      = [6, 6, 18, 6]
              lowercase				:	Any			      = [64, 128, 320, 512]
              lowercase				:	Union[str, Any]			      = 4.0
              lowercase				:	Optional[int]			      = 1e-6
              lowercase				:	Optional[Any]			      = 0.9
       elif size == "m36":
              lowercase				:	Tuple			      = [6, 6, 18, 6]
              lowercase				:	Any			      = [96, 192, 384, 768]
              lowercase				:	Dict			      = 4.0
              lowercase				:	Any			      = 1e-6
              lowercase				:	int			      = 0.95
       elif size == "m48":
              lowercase				:	int			      = [8, 8, 24, 8]
              lowercase				:	Union[str, Any]			      = [96, 192, 384, 768]
              lowercase				:	Optional[Any]			      = 4.0
              lowercase				:	Optional[int]			      = 1e-6
              lowercase				:	Optional[int]			      = 0.95
       else:
              raise ValueError(f"Size {size} not supported"							)

       # load image processor
       lowercase				:	str			      = PoolFormerImageProcessor(crop_pct=_lowerCAmelCase							)

       # Prepare image
       lowercase				:	str			      = prepare_img()
       lowercase				:	Optional[int]			      = image_processor(images=_lowerCAmelCase   ,      return_tensors="""pt"""							).pixel_values

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

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

       # rename keys
       lowercase				:	Optional[int]			      = rename_keys(_lowerCAmelCase							)

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

       # Define image processor
       lowercase				:	int			      = PoolFormerImageProcessor(crop_pct=_lowerCAmelCase							)
       lowercase				:	Any			      = image_processor(images=prepare_img()   ,      return_tensors="""pt"""							).pixel_values

       # forward pass
       lowercase				:	Tuple			      = model(_lowerCAmelCase							)
       lowercase				:	List[str]			      = outputs.logits

       # define expected logit slices for different models
       if size == "s12":
              lowercase				:	Optional[Any]			      = torch.tensor([-0.3045, -0.6758, -0.4869]							)
       elif size == "s24":
              lowercase				:	str			      = torch.tensor([0.4402, -0.1374, -0.8045]							)
       elif size == "s36":
              lowercase				:	Optional[int]			      = torch.tensor([-0.6080, -0.5133, -0.5898]							)
       elif size == "m36":
              lowercase				:	Optional[int]			      = torch.tensor([0.3952, 0.2263, -1.2668]							)
       elif size == "m48":
              lowercase				:	Dict			      = 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]   ,      _lowerCAmelCase   ,      atol=1e-2							)

       # finally, save model and image processor
       logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."							)
       Path(_lowerCAmelCase							).mkdir(exist_ok=_lowerCAmelCase							)
       model.save_pretrained(_lowerCAmelCase							)
       print(f"Saving image processor to {pytorch_dump_folder_path}"							)
       image_processor.save_pretrained(_lowerCAmelCase							)


if __name__ == "__main__":
  lowercase    :   List[str]		       =				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    :   Tuple		       =				parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''





import string

def 			a_	(				_lowerCAmelCase			) -> str:
  __lowerCamelCase			:			Union[str, Any]       			=					''
  for i in sequence:
    __lowerCamelCase			:			Tuple       			=					ord(_lowerCAmelCase			)
    if 65 <= extract <= 90:
      output += chr(155 - extract			)
    elif 97 <= extract <= 122:
      output += chr(219 - extract			)
    else:
      output += i
  return output

def 			a_	(				_lowerCAmelCase			) -> str:
  __lowerCamelCase			:			Optional[Any]       			=					string.ascii_letters
  __lowerCamelCase			:			str       			=					string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(_lowerCAmelCase			)] if c in letters else c for c in sequence			)

def 			a_	(				) -> None:
  from timeit import timeit

  print('Running performance benchmarks...'			)
  __lowerCamelCase			:			Tuple       			=					'from string import printable ; from __main__ import atbash, atbash_slow'
  print(F'> atbash_slow(): {timeit("atbash_slow(printable)"			,setup=_lowerCAmelCase			)} seconds'			)
  print(F'>      atbash(): {timeit("atbash(printable)"			,setup=_lowerCAmelCase			)} seconds'			)


if __name__ == "__main__":
   for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
      print(f'''{example} encrypted in atbash: {atbash(example)}''')
   benchmark()






