





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()
__A							:			str         =			logging.get_logger(__name__)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




    _A     =							original_name.split('.'				)[0]
    _A     =							key.split('.'				)
    _A     =							int(key_list[key_list.index(_SCREAMING_SNAKE_CASE				) - 2]				)
    _A     =							int(key_list[key_list.index(_SCREAMING_SNAKE_CASE				) - 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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """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(_SCREAMING_SNAKE_CASE							,     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(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     'mlp.fc1'							,     'output.conv1'				)
        if "mlp.fc2" in key:
            _A     =							replace_key_with_offset(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     'mlp.fc2'							,     'output.conv2'				)
        if "norm1" in key:
            _A     =							replace_key_with_offset(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     'norm1'							,     'before_norm'				)
        if "norm2" in key:
            _A     =							replace_key_with_offset(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     'norm2'							,     'after_norm'				)
        if "layer_scale_1" in key:
            _A     =							replace_key_with_offset(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     'layer_scale_1'							,     'layer_scale_1'				)
        if "layer_scale_2" in key:
            _A     =							replace_key_with_offset(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     'layer_scale_2'							,     'layer_scale_2'				)
        if "head" in key:
            _A     =							key.replace('head'							,     'classifier'				)
        _A     =							value
    return new_state_dict





def 				__lowerCAmelCase(       )							-> List[Any]:




    """simple docstring"""




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

    return image





@torch.no_grad()
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    _A     =							PoolFormerConfig()

    # set attributes based on model_name
    _A     =							'huggingface/label-files'
    _A     =							model_name[-3:]
    _A     =							1_000
    _A     =							'imagenet-1k-id2label.json'
    _A     =							(1, 1_000)

    # set config attributes
    _A     =							json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     repo_type='dataset'				)							,     'r'				)				)
    _A     =							{int(_SCREAMING_SNAKE_CASE				): 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=_SCREAMING_SNAKE_CASE				)

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

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

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

    # rename keys
    _A     =							rename_keys(_SCREAMING_SNAKE_CASE				)

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

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

    # forward pass
    _A     =							model(_SCREAMING_SNAKE_CASE				)
    _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]							,     _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__":
 __A							:			Tuple         =			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."
 )
 __A							:			List[Any]         =			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)}")





