import math



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 if initial_intensity < 0:
  raise ValueError("The value of intensity cannot be negative"       )
  # handling of negative values of initial intensity
 if angle < 0 or angle > 360:
  raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"       )
  # handling of values out of allowed range
 return initial_intensity * (math.cos(math.radians(_A       )       ) ** 2)


if __name__ == "__main__":
  import doctest

  doctest.testmod(name="""malus_law""")



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()
SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)



def 	__A		(      _A				,					_A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	original_name.split("."       )[0]
 __a							=	key.split("."       )
 __a							=	int(key_list[key_list.index(_A       ) - 2]       )
 __a							=	int(key_list[key_list.index(_A       ) - 1]       )
 __a							=	orig_block_num - offset

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



def 	__A		(      _A       ):



 """simple docstring"""


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



def 	__A		(      ):



 """simple docstring"""


 __a							=	"http://images.cocodataset.org/val2017/000000039769.jpg"
 __a							=	Image.open(requests.get(_A				,					stream=_A       ).raw       )

 return image



@torch.no_grad()
def 	__A		(      _A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	PoolFormerConfig()

 # set attributes based on model_name
 __a							=	"huggingface/label-files"
 __a							=	model_name[-3:]
 __a							=	1000
 __a							=	"imagenet-1k-id2label.json"
 __a							=	(1, 1000)

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

 # load image processor
 __a							=	PoolFormerImageProcessor(crop_pct=_A       )

 # Prepare image
 __a							=	prepare_img()
 __a							=	image_processor(images=_A				,					return_tensors="pt"       ).pixel_values

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

 # load original state dict
 __a							=	torch.load(_A				,					map_location=torch.device("cpu"       )       )

 # rename keys
 __a							=	rename_keys(_A       )

 # create HuggingFace model and load state dict
 __a							=	PoolFormerForImageClassification(_A       )
 model.load_state_dict(_A       )
 model.eval()

 # Define image processor
 __a							=	PoolFormerImageProcessor(crop_pct=_A       )
 __a							=	image_processor(images=prepare_img()				,					return_tensors="pt"       ).pixel_values

 # forward pass
 __a							=	model(_A       )
 __a							=	outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  __a							=	torch.tensor([-0.3045, -0.6758, -0.4869]       )
 elif size == "s24":
  __a							=	torch.tensor([0.4402, -0.1374, -0.8045]       )
 elif size == "s36":
  __a							=	torch.tensor([-0.6080, -0.5133, -0.5898]       )
 elif size == "m36":
  __a							=	torch.tensor([0.3952, 0.2263, -1.2668]       )
 elif size == "m48":
  __a							=	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]				,					_A				,					atol=1E-2       )

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		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."""
  )
  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)


