
"""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()
UpperCAmelCase__        =					logging.get_logger(__name__)
def    _UpperCAmelCase (					__lowerCamelCase  :			str					,   __lowerCamelCase  :			str					,   __lowerCamelCase  :			Optional[Any]					,   __lowerCamelCase  :			Optional[Any]						)	->   Tuple:
  _snake_case   	=			original_name.split('''.'''						)[0]
  _snake_case   	=			key.split('''.'''						)
  _snake_case   	=			int(key_list[key_list.index(_SCREAMING_SNAKE_CASE						) - 2]						)
  _snake_case   	=			int(key_list[key_list.index(_SCREAMING_SNAKE_CASE						) - 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    _UpperCAmelCase (					__lowerCamelCase  :			List[Any]						)	->   Optional[Any]:
  _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(_SCREAMING_SNAKE_CASE					,   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(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   '''mlp.fc1'''					,   '''output.conv1'''						)
    if "mlp.fc2" in key:
      _snake_case   	=			replace_key_with_offset(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   '''mlp.fc2'''					,   '''output.conv2'''						)
    if "norm1" in key:
      _snake_case   	=			replace_key_with_offset(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   '''norm1'''					,   '''before_norm'''						)
    if "norm2" in key:
      _snake_case   	=			replace_key_with_offset(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   '''norm2'''					,   '''after_norm'''						)
    if "layer_scale_1" in key:
      _snake_case   	=			replace_key_with_offset(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   '''layer_scale_1'''					,   '''layer_scale_1'''						)
    if "layer_scale_2" in key:
      _snake_case   	=			replace_key_with_offset(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   '''layer_scale_2'''					,   '''layer_scale_2'''						)
    if "head" in key:
      _snake_case   	=			key.replace('''head'''					,   '''classifier'''						)
    _snake_case   	=			value
  return new_state_dict
def    _UpperCAmelCase (					)	->   List[Any]:
  _snake_case   	=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case   	=			Image.open(requests.get(_SCREAMING_SNAKE_CASE					,   stream=_SCREAMING_SNAKE_CASE						).raw						)

  return image
@torch.no_grad()
def    _UpperCAmelCase (					__lowerCamelCase  :			int					,   __lowerCamelCase  :			Union[str, Any]					,   __lowerCamelCase  :			Union[str, Any]						)	->   str:
  _snake_case   	=			PoolFormerConfig()

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

  # set config attributes
  _snake_case   	=			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   repo_type='''dataset'''						)					,   '''r'''						)						)
  _snake_case   	=			{int(_SCREAMING_SNAKE_CASE						): 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, 1_28, 3_20, 5_12]
    _snake_case   	=			4.0
    _snake_case   	=			0.9
  elif size == "s24":
    _snake_case   	=			[4, 4, 12, 4]
    _snake_case   	=			[64, 1_28, 3_20, 5_12]
    _snake_case   	=			4.0
    _snake_case   	=			0.9
  elif size == "s36":
    _snake_case   	=			[6, 6, 18, 6]
    _snake_case   	=			[64, 1_28, 3_20, 5_12]
    _snake_case   	=			4.0
    _snake_case   	=			1E-6
    _snake_case   	=			0.9
  elif size == "m36":
    _snake_case   	=			[6, 6, 18, 6]
    _snake_case   	=			[96, 1_92, 3_84, 7_68]
    _snake_case   	=			4.0
    _snake_case   	=			1E-6
    _snake_case   	=			0.95
  elif size == "m48":
    _snake_case   	=			[8, 8, 24, 8]
    _snake_case   	=			[96, 1_92, 3_84, 7_68]
    _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=_SCREAMING_SNAKE_CASE						)

  # Prepare image
  _snake_case   	=			prepare_img()
  _snake_case   	=			image_processor(images=_SCREAMING_SNAKE_CASE					,   return_tensors='''pt'''						).pixel_values

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

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

  # rename keys
  _snake_case   	=			rename_keys(_SCREAMING_SNAKE_CASE						)

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

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

  # forward pass
  _snake_case   	=			model(_SCREAMING_SNAKE_CASE						)
  _snake_case   	=			outputs.logits

  # define expected logit slices for different models
  if size == "s12":
    _snake_case   	=			torch.tensor([-0.3_045, -0.6_758, -0.4_869]						)
  elif size == "s24":
    _snake_case   	=			torch.tensor([0.4_402, -0.1_374, -0.8_045]						)
  elif size == "s36":
    _snake_case   	=			torch.tensor([-0.6_080, -0.5_133, -0.5_898]						)
  elif size == "m36":
    _snake_case   	=			torch.tensor([0.3_952, 0.2_263, -1.2_668]						)
  elif size == "m48":
    _snake_case   	=			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__":
 UpperCAmelCase__        =					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.'
 )
 UpperCAmelCase__        =					parser.parse_args()
 convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> bool:




    """simple docstring"""




    if not isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        raise ValueError('check_bouncy() accepts only integer arguments'				)
    _A     =							str(_SCREAMING_SNAKE_CASE				)
    _A     =							''.join(sorted(_SCREAMING_SNAKE_CASE				)				)
    return sorted_str_n != str_n and sorted_str_n[::-1] != str_n





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 99				)							-> int:




    """simple docstring"""




    if not 0 < percent < 100:
        raise ValueError('solution() only accepts values from 0 to 100'				)
    _A     =							0
    _A     =							1

    while True:
        if check_bouncy(_SCREAMING_SNAKE_CASE				):
            bouncy_num += 1
        if (bouncy_num / num) * 100 >= percent:
            return num
        num += 1


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 print(f"{solution(99)}")





