





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      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase  ):
   '''simple docstring'''


   A_  :					Dict         =						original_name.split("""."""  )[0]
   A_  :					Tuple         =						key.split("""."""  )
   A_  :					Optional[Any]         =						int(key_list[key_list.index(_lowerCAmelCase  ) - 2]  )
   A_  :					Optional[int]         =						int(key_list[key_list.index(_lowerCAmelCase  ) - 1]  )
   A_  :					List[str]         =						orig_block_num - offset

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



def      _lowerCAmelCase		(	_lowerCAmelCase  ):
   '''simple docstring'''


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



def      _lowerCAmelCase		(	):
   '''simple docstring'''


   A_  :					Tuple         =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   A_  :					int         =						Image.open(requests.get(_lowerCAmelCase	,stream=_lowerCAmelCase  ).raw  )

   return image



@torch.no_grad()
def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase  ):
   '''simple docstring'''


   A_  :					Union[str, Any]         =						PoolFormerConfig()

   # set attributes based on model_name
   A_  :					Dict         =						"""huggingface/label-files"""
   A_  :					int         =						model_name[-3:]
   A_  :					Any         =						1_0_0_0
   A_  :					List[Any]         =						"""imagenet-1k-id2label.json"""
   A_  :					int         =						(1, 1_0_0_0)

   # set config attributes
   A_  :					Any         =						json.load(open(hf_hub_download(_lowerCAmelCase	,_lowerCAmelCase	,repo_type="""dataset"""  )	,"""r"""  )  )
   A_  :					str         =						{int(_lowerCAmelCase  ): v for k, v in idalabel.items()}
   A_  :					Dict         =						idalabel
   A_  :					Optional[int]         =						{v: k for k, v in idalabel.items()}
   if size == "s12":
      A_  :					Union[str, Any]         =						[2, 2, 6, 2]
      A_  :					int         =						[6_4, 1_2_8, 3_2_0, 5_1_2]
      A_  :					List[str]         =						4.0
      A_  :					Optional[Any]         =						0.9
   elif size == "s24":
      A_  :					Optional[int]         =						[4, 4, 1_2, 4]
      A_  :					Optional[Any]         =						[6_4, 1_2_8, 3_2_0, 5_1_2]
      A_  :					Any         =						4.0
      A_  :					str         =						0.9
   elif size == "s36":
      A_  :					Optional[Any]         =						[6, 6, 1_8, 6]
      A_  :					Union[str, Any]         =						[6_4, 1_2_8, 3_2_0, 5_1_2]
      A_  :					Optional[Any]         =						4.0
      A_  :					List[str]         =						1e-6
      A_  :					Optional[Any]         =						0.9
   elif size == "m36":
      A_  :					str         =						[6, 6, 1_8, 6]
      A_  :					str         =						[9_6, 1_9_2, 3_8_4, 7_6_8]
      A_  :					str         =						4.0
      A_  :					List[Any]         =						1e-6
      A_  :					List[str]         =						0.95
   elif size == "m48":
      A_  :					Tuple         =						[8, 8, 2_4, 8]
      A_  :					Union[str, Any]         =						[9_6, 1_9_2, 3_8_4, 7_6_8]
      A_  :					Any         =						4.0
      A_  :					str         =						1e-6
      A_  :					List[str]         =						0.95
   else:
      raise ValueError(f"""Size {size} not supported"""  )

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

   # Prepare image
   A_  :					str         =						prepare_img()
   A_  :					Tuple         =						image_processor(images=_lowerCAmelCase	,return_tensors="""pt"""  ).pixel_values

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

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

   # rename keys
   A_  :					List[Any]         =						rename_keys(_lowerCAmelCase  )

   # create HuggingFace model and load state dict
   A_  :					List[str]         =						PoolFormerForImageClassification(_lowerCAmelCase  )
   model.load_state_dict(_lowerCAmelCase  )
   model.eval()

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

   # forward pass
   A_  :					Tuple         =						model(_lowerCAmelCase  )
   A_  :					Any         =						outputs.logits

   # define expected logit slices for different models
   if size == "s12":
      A_  :					Any         =						torch.tensor([-0.3045, -0.6758, -0.4869]  )
   elif size == "s24":
      A_  :					Dict         =						torch.tensor([0.4402, -0.1374, -0.8045]  )
   elif size == "s36":
      A_  :					str         =						torch.tensor([-0.6080, -0.5133, -0.5898]  )
   elif size == "m36":
      A_  :					Any         =						torch.tensor([0.3952, 0.2263, -1.2668]  )
   elif size == "m48":
      A_  :					Any         =						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__":
      _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 typing import Any

import numpy as np
def 		_a       ( a					:np.ndarray					)				->     bool:
 return np.array_equal(a      ,				matrix.conjugate().T					)
def 		_a       ( a					:np.ndarray      ,				a					:np.ndarray					)				->     Any:
 a						    = v.conjugate().T
 a						    = v_star.dot(a					)
 assert isinstance(a      ,				np.ndarray					)
 return (v_star_dot.dot(a					)) / (v_star.dot(a					))
def 		_a       ( )				->     None:
 a						    = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]					)
 a						    = np.array([[1], [2], [3]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 print(rayleigh_quotient(a      ,				a					)					)

 a						    = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 assert rayleigh_quotient(a      ,				a					) == float(3					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     tests()


