

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	str						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      original_name.split('.'						)[0]
 SCREAMING_SNAKE_CASE		    =      key.split('.'						)
 SCREAMING_SNAKE_CASE		    =      int(key_list[key_list.index(_UpperCamelCase						) - 2]						)
 SCREAMING_SNAKE_CASE		    =      int(key_list[key_list.index(_UpperCamelCase						) - 1]						)
 SCREAMING_SNAKE_CASE		    =      orig_block_num - offset

 SCREAMING_SNAKE_CASE		    =      key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""		,				f"""block.{new_block_num}.{layer_num}.{new_name}"""						)
 return key


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				int:
 '''simple docstring'''


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


def 							__lowerCAmelCase     (							)		->				List[Any]:
 '''simple docstring'''


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

 return image


@torch.no_grad()
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	int						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      PoolFormerConfig()

 # set attributes based on model_name
 SCREAMING_SNAKE_CASE		    =      'huggingface/label-files'
 SCREAMING_SNAKE_CASE		    =      model_name[-3:]
 SCREAMING_SNAKE_CASE		    =      10_00
 SCREAMING_SNAKE_CASE		    =      'imagenet-1k-id2label.json'
 SCREAMING_SNAKE_CASE		    =      (1, 10_00)

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

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

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

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

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

 # rename keys
 SCREAMING_SNAKE_CASE		    =      rename_keys(_UpperCamelCase						)

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

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

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

 # define expected logit slices for different models
 if size == "s12":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([-0.30_45, -0.67_58, -0.48_69]						)
 elif size == "s24":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([0.44_02, -0.13_74, -0.80_45]						)
 elif size == "s36":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([-0.60_80, -0.51_33, -0.58_98]						)
 elif size == "m36":
  SCREAMING_SNAKE_CASE		    =      torch.tensor([0.39_52, 0.22_63, -1.26_68]						)
 elif size == "m48":
  SCREAMING_SNAKE_CASE		    =      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]		,				_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__":
      a_     :    Tuple     		=   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_     :    str     		=   parser.parse_args()
      convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)




import string


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      ''
 for i in sequence:
  SCREAMING_SNAKE_CASE		    =      ord(_UpperCamelCase						)
  if 65 <= extract <= 90:
   output += chr(1_55 - extract						)
  elif 97 <= extract <= 1_22:
   output += chr(2_19 - extract						)
  else:
   output += i
 return output


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      string.ascii_letters
 SCREAMING_SNAKE_CASE		    =      string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
 return "".join(
     letters_reversed[letters.index(_UpperCamelCase						)] if c in letters else c for c in sequence						)


def 							__lowerCAmelCase     (							)		->				None:
 '''simple docstring'''


 from timeit import timeit

 print('Running performance benchmarks...'						)
 SCREAMING_SNAKE_CASE		    =      'from string import printable ; from __main__ import atbash, atbash_slow'
 print(f"""> atbash_slow(): {timeit('atbash_slow(printable)'		,				setup=_UpperCamelCase						)} seconds"""						)
 print(f""">      atbash(): {timeit('atbash(printable)'		,				setup=_UpperCamelCase						)} seconds"""						)


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

