





'''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()
lowercase__    :      Optional[int]						=    logging.get_logger(__name__)






def        _lowerCAmelCase			(  __snake_case  : Optional[int] ,							__snake_case  : int ,							__snake_case  : Dict ,							__snake_case  : str			)     ->     Union[str, Any]:
     __A						: Optional[int]      =       original_name.split('.'			)[0]
     __A						: Tuple      =       key.split('.'			)
     __A						: Dict      =       int(key_list[key_list.index(__snake_case			) - 2]			)
     __A						: Tuple      =       int(key_list[key_list.index(__snake_case			) - 1]			)
     __A						: str      =       orig_block_num - offset

     __A						: Optional[Any]      =       key.replace(f'{orig_block_num}.{layer_num}.{original_name}' ,							f'block.{new_block_num}.{layer_num}.{new_name}'			)
     return key






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






def        _lowerCAmelCase			(  )     ->     str:
     __A						: Tuple      =       'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A						: Optional[int]      =       Image.open(requests.get(__snake_case ,							stream=__snake_case			).raw			)

     return image






@torch.no_grad()
def        _lowerCAmelCase			(  __snake_case  : List[Any] ,							__snake_case  : Optional[int] ,							__snake_case  : Optional[Any]			)     ->     Union[str, Any]:
     __A						: str      =       PoolFormerConfig()

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

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

     # load image processor
     __A						: List[str]      =       PoolFormerImageProcessor(crop_pct=__snake_case			)

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

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

     # load original state dict
     __A						: str      =       torch.load(__snake_case ,							map_location=torch.device('cpu'			)			)

     # rename keys
     __A						: Optional[int]      =       rename_keys(__snake_case			)

     # create HuggingFace model and load state dict
     __A						: List[Any]      =       PoolFormerForImageClassification(__snake_case			)
     model.load_state_dict(__snake_case			)
     model.eval()

     # Define image processor
     __A						: List[str]      =       PoolFormerImageProcessor(crop_pct=__snake_case			)
     __A						: List[Any]      =       image_processor(images=prepare_img() ,							return_tensors='pt'			).pixel_values

     # forward pass
     __A						: Dict      =       model(__snake_case			)
     __A						: Tuple      =       outputs.logits

     # define expected logit slices for different models
     if size == "s12":
          __A						: Optional[Any]      =       torch.tensor([-0.3_045, -0.6_758, -0.4_869]			)
     elif size == "s24":
          __A						: int      =       torch.tensor([0.4_402, -0.1_374, -0.8_045]			)
     elif size == "s36":
          __A						: str      =       torch.tensor([-0.6_080, -0.5_133, -0.5_898]			)
     elif size == "m36":
          __A						: Union[str, Any]      =       torch.tensor([0.3_952, 0.2_263, -1.2_668]			)
     elif size == "m48":
          __A						: Dict      =       torch.tensor([0.1_167, -0.0_656, -0.3_423]			)
     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__":
  lowercase__    :      Dict						=    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__    :      Union[str, Any]						=    parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''




from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray






def        _lowerCAmelCase			(  __snake_case  : float ,							__snake_case  : float ,							__snake_case  : bool = False			)     ->     list[float]:
     if radian_mode:
          return [magnitude * cos(__snake_case			), magnitude * sin(__snake_case			)]
     return [magnitude * cos(radians(__snake_case			)			), magnitude * sin(radians(__snake_case			)			)]






def        _lowerCAmelCase			(  __snake_case  : NDArray[floataa] ,							__snake_case  : NDArray[floataa] ,							__snake_case  : float = 10**-1			)     ->     bool:
     __A						: NDArray[floataa]      =       cross(__snake_case ,							__snake_case			)
     __A						: float      =       sum(__snake_case			)
     return abs(__snake_case			) < eps


if __name__ == "__main__":
  # Test to check if it works
  lowercase__    :      List[Any]						=    array(
      [
          polar_force(718.4, 1_80 - 30),
          polar_force(879.54, 45),
          polar_force(1_00, -90),
      ]
  )

  lowercase__    :      NDArray[floataa]						=    array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem 1 in image_data/2D_problems.jpg
  lowercase__    :      Dict						=    array(
      [
          polar_force(30 * 9.81, 15),
          polar_force(2_15, 1_80 - 45),
          polar_force(2_64, 90 - 30),
      ]
  )

  lowercase__    :      Any						=    array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem in image_data/2D_problems_1.jpg
  lowercase__    :      Optional[int]						=    array([[0, -20_00], [0, -12_00], [0, 1_56_00], [0, -1_24_00]])

  lowercase__    :      Optional[int]						=    array([[0, 0], [6, 0], [10, 0], [12, 0]])

  assert in_static_equilibrium(forces, location)

  import doctest

  doctest.testmod()