


"""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()
lowerCAmelCase__      				=   logging.get_logger(__name__)
def 					a__			(				SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    Optional[Any]				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    Tuple				):


  '''simple docstring'''







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

  lowerCAmelCase     :     Optional[int]								=							key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""				,      f"""block.{new_block_num}.{layer_num}.{new_name}"""				)
  return key
def 					a__			(				SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







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


  '''simple docstring'''







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

  return image
@torch.no_grad()
def 					a__			(				SCREAMING_SNAKE_CASE    :    Union[str, Any]				,      SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  lowerCAmelCase     :     str								=							PoolFormerConfig()

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

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

  # load image processor
  lowerCAmelCase     :     Optional[Any]								=							PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE				)

  # Prepare image
  lowerCAmelCase     :     Dict								=							prepare_img()
  lowerCAmelCase     :     str								=							image_processor(images=SCREAMING_SNAKE_CASE				,      return_tensors="pt"				).pixel_values

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

  # load original state dict
  lowerCAmelCase     :     Dict								=							torch.load(SCREAMING_SNAKE_CASE				,      map_location=torch.device("cpu"				)				)

  # rename keys
  lowerCAmelCase     :     str								=							rename_keys(SCREAMING_SNAKE_CASE				)

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

  # Define image processor
  lowerCAmelCase     :     Any								=							PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     List[Any]								=							image_processor(images=prepare_img()				,      return_tensors="pt"				).pixel_values

  # forward pass
  lowerCAmelCase     :     List[Any]								=							model(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Dict								=							outputs.logits

  # define expected logit slices for different models
  if size == "s12":
    lowerCAmelCase     :     Optional[Any]								=							torch.tensor([-0.3_045, -0.6_758, -0.4_869]				)
  elif size == "s24":
    lowerCAmelCase     :     Any								=							torch.tensor([0.4_402, -0.1_374, -0.8_045]				)
  elif size == "s36":
    lowerCAmelCase     :     int								=							torch.tensor([-0.6_080, -0.5_133, -0.5_898]				)
  elif size == "m36":
    lowerCAmelCase     :     str								=							torch.tensor([0.3_952, 0.2_263, -1.2_668]				)
  elif size == "m48":
    lowerCAmelCase     :     Optional[Any]								=							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]				,      SCREAMING_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(SCREAMING_SNAKE_CASE				).mkdir(exist_ok=SCREAMING_SNAKE_CASE				)
  model.save_pretrained(SCREAMING_SNAKE_CASE				)
  print(f"""Saving image processor to {pytorch_dump_folder_path}"""				)
  image_processor.save_pretrained(SCREAMING_SNAKE_CASE				)


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)







"""simple docstring"""





from __future__ import annotations

import math







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Any			     =    u
   for i in range(1,		__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    temp * (u - i)
   return temp







def 	__a     (		):
   UpperCAmelCase_					:					str			     =    int(input("enter the numbers of values: "  )  )
   UpperCAmelCase_					:					list[list[float]]			     =    []
   for _ in range(__lowerCamelCase  ):
      y.append([]  )
   for i in range(__lowerCamelCase  ):
      for j in range(__lowerCamelCase  ):
         y[i].append(__lowerCamelCase  )
         UpperCAmelCase_					:					Tuple			     =    0

   print("enter the values of parameters in a list: "  )
   UpperCAmelCase_					:					Union[str, Any]			     =    list(map(__lowerCamelCase,		input().split()  )  )

   print("enter the values of corresponding parameters: "  )
   for i in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    float(input()  )

   UpperCAmelCase_					:					Tuple			     =    int(input("enter the value to interpolate: "  )  )
   UpperCAmelCase_					:					Tuple			     =    (value - x[0]) / (x[1] - x[0])

   # for calculating forward difference table

   for i in range(1,		__lowerCamelCase  ):
      for j in range(n - i  ):
         UpperCAmelCase_					:					Union[str, Any]			     =    y[j + 1][i - 1] - y[j][i - 1]

   UpperCAmelCase_					:					Optional[int]			     =    y[0][0]
   for i in range(1,		__lowerCamelCase  ):
      summ += (ucal(__lowerCamelCase,		__lowerCamelCase  ) * y[0][i]) / math.factorial(__lowerCamelCase  )

   print(f"""the value at {value} is {summ}"""  )


if __name__ == "__main__":
    main()


