




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()
lowerCAmelCase_						=			logging.get_logger(__name__)






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							Union[str, Any]:





     '''simple docstring'''


     lowercase :       Tuple       =			original_name.split('''.'''   )[0]
     lowercase :       Optional[Any]       =			key.split('''.'''   )
     lowercase :       Union[str, Any]       =			int(key_list[key_list.index(__magic_name__   ) - 2]   )
     lowercase :       Dict       =			int(key_list[key_list.index(__magic_name__   ) - 1]   )
     lowercase :       str       =			orig_block_num - offset

     lowercase :       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  snake_case(    __magic_name__   )			->							Tuple:





     '''simple docstring'''


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






def  snake_case(    )			->							str:





     '''simple docstring'''


     lowercase :       Optional[Any]       =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     lowercase :       str       =			Image.open(requests.get(__magic_name__			,		stream=__magic_name__   ).raw   )

     return image






@torch.no_grad()
def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							List[Any]:





     '''simple docstring'''


     lowercase :       Optional[int]       =			PoolFormerConfig()

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

     # set config attributes
     lowercase :       Dict       =			json.load(open(hf_hub_download(__magic_name__			,		__magic_name__			,		repo_type='''dataset'''   )			,		'''r'''   )   )
     lowercase :       Union[str, Any]       =			{int(__magic_name__   ): v for k, v in idalabel.items()}
     lowercase :       Optional[Any]       =			idalabel
     lowercase :       Optional[int]       =			{v: k for k, v in idalabel.items()}
     if size == "s12":
          lowercase :       Any       =			[2, 2, 6, 2]
          lowercase :       int       =			[64, 1_28, 3_20, 5_12]
          lowercase :       List[str]       =			4.0
          lowercase :       Optional[Any]       =			0.9
     elif size == "s24":
          lowercase :       Any       =			[4, 4, 12, 4]
          lowercase :       Any       =			[64, 1_28, 3_20, 5_12]
          lowercase :       List[Any]       =			4.0
          lowercase :       str       =			0.9
     elif size == "s36":
          lowercase :       Dict       =			[6, 6, 18, 6]
          lowercase :       Dict       =			[64, 1_28, 3_20, 5_12]
          lowercase :       Optional[int]       =			4.0
          lowercase :       List[str]       =			1e-6
          lowercase :       Optional[Any]       =			0.9
     elif size == "m36":
          lowercase :       Union[str, Any]       =			[6, 6, 18, 6]
          lowercase :       Tuple       =			[96, 1_92, 3_84, 7_68]
          lowercase :       List[Any]       =			4.0
          lowercase :       Union[str, Any]       =			1e-6
          lowercase :       List[str]       =			0.9_5
     elif size == "m48":
          lowercase :       List[str]       =			[8, 8, 24, 8]
          lowercase :       Tuple       =			[96, 1_92, 3_84, 7_68]
          lowercase :       str       =			4.0
          lowercase :       Dict       =			1e-6
          lowercase :       List[str]       =			0.9_5
     else:
          raise ValueError(F"""Size {size} not supported"""   )

     # load image processor
     lowercase :       List[str]       =			PoolFormerImageProcessor(crop_pct=__magic_name__   )

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

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

     # load original state dict
     lowercase :       List[Any]       =			torch.load(__magic_name__			,		map_location=torch.device('''cpu'''   )   )

     # rename keys
     lowercase :       str       =			rename_keys(__magic_name__   )

     # create HuggingFace model and load state dict
     lowercase :       Optional[Any]       =			PoolFormerForImageClassification(__magic_name__   )
     model.load_state_dict(__magic_name__   )
     model.eval()

     # Define image processor
     lowercase :       List[str]       =			PoolFormerImageProcessor(crop_pct=__magic_name__   )
     lowercase :       Optional[int]       =			image_processor(images=prepare_img()			,		return_tensors='''pt'''   ).pixel_values

     # forward pass
     lowercase :       Optional[Any]       =			model(__magic_name__   )
     lowercase :       Union[str, Any]       =			outputs.logits

     # define expected logit slices for different models
     if size == "s12":
          lowercase :       Dict       =			torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]   )
     elif size == "s24":
          lowercase :       Optional[Any]       =			torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]   )
     elif size == "s36":
          lowercase :       Tuple       =			torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]   )
     elif size == "m36":
          lowercase :       Union[str, Any]       =			torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]   )
     elif size == "m48":
          lowercase :       Dict       =			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]			,		__magic_name__			,		atol=1e-2   )

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


if __name__ == "__main__":
 lowerCAmelCase_						=			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.'
 )
 lowerCAmelCase_						=			parser.parse_args()
 convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





from string import ascii_uppercase

lowerCAmelCase_						=			{char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase_						=			dict(enumerate(ascii_uppercase))






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Optional[Any]       =			len(__magic_name__   )
     lowercase :       Any       =			0
     while True:
          if x == i:
               lowercase :       Any       =			0
          if len(__magic_name__   ) == len(__magic_name__   ):
               break
          key += key[i]
          i += 1
     return key






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       str       =			''''''
     lowercase :       Dict       =			0
     for letter in message:
          if letter == " ":
               cipher_text += " "
          else:
               lowercase :       Dict       =			(dicta[letter] - dicta[key_new[i]]) % 26
               i += 1
               cipher_text += dicta[x]
     return cipher_text






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Any       =			''''''
     lowercase :       str       =			0
     for letter in cipher_text:
          if letter == " ":
               or_txt += " "
          else:
               lowercase :       Any       =			(dicta[letter] + dicta[key_new[i]] + 26) % 26
               i += 1
               or_txt += dicta[x]
     return or_txt






def  snake_case(    )			->							None:





     '''simple docstring'''


     lowercase :       Dict       =			'''THE GERMAN ATTACK'''
     lowercase :       Dict       =			'''SECRET'''
     lowercase :       Union[str, Any]       =			generate_key(__magic_name__			,		__magic_name__   )
     lowercase :       List[str]       =			cipher_text(__magic_name__			,		__magic_name__   )
     print(F"""Encrypted Text = {s}"""   )
     print(F"""Original Text = {original_text(__magic_name__			,		__magic_name__   )}"""   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()