


"""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()
snake_case__    				=				logging.get_logger(__name__)


def      _snake_case						(						_snake_case				:			Union[str, Any] ,   _snake_case				:			Union[str, Any] ,   _snake_case				:			str ,   _snake_case				:			List[str]			):
      lowerCAmelCase					:	Tuple       =   original_name.split('''.'''			)[0]
      lowerCAmelCase					:	Optional[int]       =   key.split('''.'''			)
      lowerCAmelCase					:	Tuple       =   int(key_list[key_list.index(_snake_case			) - 2]			)
      lowerCAmelCase					:	List[str]       =   int(key_list[key_list.index(_snake_case			) - 1]			)
      lowerCAmelCase					:	Optional[int]       =   orig_block_num - offset

      lowerCAmelCase					:	Tuple       =   key.replace(f'''{orig_block_num}.{layer_num}.{original_name}''' ,   f'''block.{new_block_num}.{layer_num}.{new_name}'''			)
      return key


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


def      _snake_case						(						):
      lowerCAmelCase					:	List[Any]       =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase					:	Optional[int]       =   Image.open(requests.get(_snake_case ,   stream=_snake_case			).raw			)

      return image


@torch.no_grad()
def      _snake_case						(						_snake_case				:			Optional[Any] ,   _snake_case				:			int ,   _snake_case				:			List[str]			):
      lowerCAmelCase					:	Dict       =   PoolFormerConfig()

      # set attributes based on model_name
      lowerCAmelCase					:	int       =   '''huggingface/label-files'''
      lowerCAmelCase					:	Optional[int]       =   model_name[-3:]
      lowerCAmelCase					:	Union[str, Any]       =   1000
      lowerCAmelCase					:	Any       =   '''imagenet-1k-id2label.json'''
      lowerCAmelCase					:	Dict       =   (1, 1000)

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

      # load image processor
      lowerCAmelCase					:	List[str]       =   PoolFormerImageProcessor(crop_pct=_snake_case			)

      # Prepare image
      lowerCAmelCase					:	Dict       =   prepare_img()
      lowerCAmelCase					:	int       =   image_processor(images=_snake_case ,   return_tensors='''pt'''			).pixel_values

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

      # load original state dict
      lowerCAmelCase					:	Tuple       =   torch.load(_snake_case ,   map_location=torch.device('''cpu'''			)			)

      # rename keys
      lowerCAmelCase					:	List[str]       =   rename_keys(_snake_case			)

      # create HuggingFace model and load state dict
      lowerCAmelCase					:	Dict       =   PoolFormerForImageClassification(_snake_case			)
      model.load_state_dict(_snake_case			)
      model.eval()

      # Define image processor
      lowerCAmelCase					:	Tuple       =   PoolFormerImageProcessor(crop_pct=_snake_case			)
      lowerCAmelCase					:	str       =   image_processor(images=prepare_img() ,   return_tensors='''pt'''			).pixel_values

      # forward pass
      lowerCAmelCase					:	Optional[Any]       =   model(_snake_case			)
      lowerCAmelCase					:	int       =   outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            lowerCAmelCase					:	Union[str, Any]       =   torch.tensor([-0.3045, -0.6758, -0.4869]			)
      elif size == "s24":
            lowerCAmelCase					:	Any       =   torch.tensor([0.4402, -0.1374, -0.8045]			)
      elif size == "s36":
            lowerCAmelCase					:	Union[str, Any]       =   torch.tensor([-0.6080, -0.5133, -0.5898]			)
      elif size == "m36":
            lowerCAmelCase					:	List[str]       =   torch.tensor([0.3952, 0.2263, -1.2668]			)
      elif size == "m48":
            lowerCAmelCase					:	int       =   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] ,   _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__":
 snake_case__    				=				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.'''
 )
 snake_case__    				=				parser.parse_args()
 convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""





def      _snake_case						(						_snake_case				:			float ,   _snake_case				:			list[float]			):
      if discount_rate < 0:
            raise ValueError('''Discount rate cannot be negative'''			)
      if not cash_flows:
            raise ValueError('''Cash flows list cannot be empty'''			)
      lowerCAmelCase					:	List[str]       =   sum(
          cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_snake_case			)			)
      return round(_snake_case ,   ndigits=2			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


