


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__							:						str							     =   logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				List[Any]:
  snake_case__						= original_name.split('''.'''				)[0]
  snake_case__						= key.split('''.'''				)
  snake_case__						= int(key_list[key_list.index(lowerCAmelCase__				) - 2]				)
  snake_case__						= int(key_list[key_list.index(lowerCAmelCase__				) - 1]				)
  snake_case__						= orig_block_num - offset

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







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Tuple:
  snake_case__						= OrderedDict()
  snake_case__				,				snake_case__						= 0, 0
  for key, value in state_dict.items():
    if key.startswith('''network'''				):
      snake_case__						= 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
      snake_case__						= key[: key.find('''proj'''				)]
      snake_case__						= key.replace(lowerCAmelCase__							,   F"""patch_embeddings.{total_embed_found}."""				)
      snake_case__						= key.replace('''proj'''							,   '''projection'''				)
      if key.endswith('''bias'''				):
        total_embed_found += 1
    if "patch_embeddings" in key:
      snake_case__						= '''poolformer.encoder.''' + key
    if "mlp.fc1" in key:
      snake_case__						= replace_key_with_offset(lowerCAmelCase__							,   lowerCAmelCase__							,   '''mlp.fc1'''							,   '''output.conv1'''				)
    if "mlp.fc2" in key:
      snake_case__						= replace_key_with_offset(lowerCAmelCase__							,   lowerCAmelCase__							,   '''mlp.fc2'''							,   '''output.conv2'''				)
    if "norm1" in key:
      snake_case__						= replace_key_with_offset(lowerCAmelCase__							,   lowerCAmelCase__							,   '''norm1'''							,   '''before_norm'''				)
    if "norm2" in key:
      snake_case__						= replace_key_with_offset(lowerCAmelCase__							,   lowerCAmelCase__							,   '''norm2'''							,   '''after_norm'''				)
    if "layer_scale_1" in key:
      snake_case__						= replace_key_with_offset(lowerCAmelCase__							,   lowerCAmelCase__							,   '''layer_scale_1'''							,   '''layer_scale_1'''				)
    if "layer_scale_2" in key:
      snake_case__						= replace_key_with_offset(lowerCAmelCase__							,   lowerCAmelCase__							,   '''layer_scale_2'''							,   '''layer_scale_2'''				)
    if "head" in key:
      snake_case__						= key.replace('''head'''							,   '''classifier'''				)
    snake_case__						= value
  return new_state_dict







def SCREAMING_SNAKE_CASE	(			)    ->				str:
  snake_case__						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  snake_case__						= Image.open(requests.get(lowerCAmelCase__							,   stream=lowerCAmelCase__				).raw				)

  return image







@torch.no_grad()
def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				Optional[int]:
  snake_case__						= PoolFormerConfig()

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

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

  # load image processor
  snake_case__						= PoolFormerImageProcessor(crop_pct=lowerCAmelCase__				)

  # Prepare image
  snake_case__						= prepare_img()
  snake_case__						= image_processor(images=lowerCAmelCase__							,   return_tensors='''pt'''				).pixel_values

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

  # load original state dict
  snake_case__						= torch.load(lowerCAmelCase__							,   map_location=torch.device('''cpu'''				)				)

  # rename keys
  snake_case__						= rename_keys(lowerCAmelCase__				)

  # create HuggingFace model and load state dict
  snake_case__						= PoolFormerForImageClassification(lowerCAmelCase__				)
  model.load_state_dict(lowerCAmelCase__				)
  model.eval()

  # Define image processor
  snake_case__						= PoolFormerImageProcessor(crop_pct=lowerCAmelCase__				)
  snake_case__						= image_processor(images=prepare_img()							,   return_tensors='''pt'''				).pixel_values

  # forward pass
  snake_case__						= model(lowerCAmelCase__				)
  snake_case__						= outputs.logits

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




'''simple docstring'''

import os

# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE							:				List[Any]     	=			[int(0.5 * n * (n + 1)) for n in range(1, 101)]




def 							_UpperCamelCase				(   )      -> List[Any]:
							SCREAMING_SNAKE_CASE_				=				os.path.dirname(os.path.realpath(lowerCAmelCase__   )   )
							SCREAMING_SNAKE_CASE_				=				os.path.join(lowerCAmelCase__		,'words.txt'   )

							SCREAMING_SNAKE_CASE_				=				''
							with open(lowerCAmelCase__   ) as f:
														SCREAMING_SNAKE_CASE_				=				f.readline()

							SCREAMING_SNAKE_CASE_				=				[word.strip('"'   ) for word in words.strip('\r\n'   ).split(','   )]
							SCREAMING_SNAKE_CASE_				=				[
							    word
							    for word in [sum(ord(lowerCAmelCase__   ) - 64 for x in word   ) for word in words]
							    if word in TRIANGULAR_NUMBERS
							]
							return len(lowerCAmelCase__   )


if __name__ == "__main__":
							print(solution())





