
"""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 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      int     							=					original_name.split('.' )[0]
    UpperCAmelCase__	:      Tuple     							=					key.split('.' )
    UpperCAmelCase__	:      Optional[Any]     							=					int(key_list[key_list.index(_snake_case ) - 2] )
    UpperCAmelCase__	:      int     							=					int(key_list[key_list.index(_snake_case ) - 1] )
    UpperCAmelCase__	:      Dict     							=					orig_block_num - offset

    UpperCAmelCase__	:      str     							=					key.replace(F'''{orig_block_num}.{layer_num}.{original_name}'''    ,F'''block.{new_block_num}.{layer_num}.{new_name}''' )
    return key
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Union[str, Any]     							=					OrderedDict()
    UpperCAmelCase__							,							UpperCAmelCase__	:      Dict     							=					0, 0
    for key, value in state_dict.items():
        if key.startswith('network' ):
            UpperCAmelCase__	:      Optional[int]     							=					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
            UpperCAmelCase__	:      int     							=					key[: key.find('proj' )]
            UpperCAmelCase__	:      str     							=					key.replace(_snake_case    ,F'''patch_embeddings.{total_embed_found}.''' )
            UpperCAmelCase__	:      Dict     							=					key.replace('proj'    ,'projection' )
            if key.endswith('bias' ):
                total_embed_found += 1
        if "patch_embeddings" in key:
            UpperCAmelCase__	:      int     							=					'poolformer.encoder.' + key
        if "mlp.fc1" in key:
            UpperCAmelCase__	:      Union[str, Any]     							=					replace_key_with_offset(_snake_case    ,_snake_case    ,'mlp.fc1'    ,'output.conv1' )
        if "mlp.fc2" in key:
            UpperCAmelCase__	:      str     							=					replace_key_with_offset(_snake_case    ,_snake_case    ,'mlp.fc2'    ,'output.conv2' )
        if "norm1" in key:
            UpperCAmelCase__	:      int     							=					replace_key_with_offset(_snake_case    ,_snake_case    ,'norm1'    ,'before_norm' )
        if "norm2" in key:
            UpperCAmelCase__	:      Union[str, Any]     							=					replace_key_with_offset(_snake_case    ,_snake_case    ,'norm2'    ,'after_norm' )
        if "layer_scale_1" in key:
            UpperCAmelCase__	:      Any     							=					replace_key_with_offset(_snake_case    ,_snake_case    ,'layer_scale_1'    ,'layer_scale_1' )
        if "layer_scale_2" in key:
            UpperCAmelCase__	:      List[Any]     							=					replace_key_with_offset(_snake_case    ,_snake_case    ,'layer_scale_2'    ,'layer_scale_2' )
        if "head" in key:
            UpperCAmelCase__	:      Tuple     							=					key.replace('head'    ,'classifier' )
        UpperCAmelCase__	:      Optional[int]     							=					value
    return new_state_dict
def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      int     							=					'http://images.cocodataset.org/val2017/000000039769.jpg'
    UpperCAmelCase__	:      Optional[int]     							=					Image.open(requests.get(_snake_case    ,stream=_snake_case ).raw )

    return image
@torch.no_grad()
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Tuple     							=					PoolFormerConfig()

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

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

    # load image processor
    UpperCAmelCase__	:      str     							=					PoolFormerImageProcessor(crop_pct=_snake_case )

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

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

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

    # rename keys
    UpperCAmelCase__	:      str     							=					rename_keys(_snake_case )

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

    # Define image processor
    UpperCAmelCase__	:      Union[str, Any]     							=					PoolFormerImageProcessor(crop_pct=_snake_case )
    UpperCAmelCase__	:      str     							=					image_processor(images=prepare_img()    ,return_tensors='pt' ).pixel_values

    # forward pass
    UpperCAmelCase__	:      Tuple     							=					model(_snake_case )
    UpperCAmelCase__	:      int     							=					outputs.logits

    # define expected logit slices for different models
    if size == "s12":
        UpperCAmelCase__	:      Optional[int]     							=					torch.tensor([-0.3045, -0.6758, -0.4869] )
    elif size == "s24":
        UpperCAmelCase__	:      Dict     							=					torch.tensor([0.4402, -0.1374, -0.8045] )
    elif size == "s36":
        UpperCAmelCase__	:      List[Any]     							=					torch.tensor([-0.6080, -0.5133, -0.5898] )
    elif size == "m36":
        UpperCAmelCase__	:      Optional[int]     							=					torch.tensor([0.3952, 0.2263, -1.2668] )
    elif size == "m48":
        UpperCAmelCase__	:      Any     							=					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__":
    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)



"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					value
          UpperCAmelCase__	:      Node | None     							=					None
          UpperCAmelCase__	:      Node | None     							=					None





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					tree



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left      ) + self.depth_first_search(node.right      )
          )



      def __iter__(   self      ):
          yield self.depth_first_search(self.tree      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

