
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						      =				logging.get_logger(__name__)



def 		lowercase_    (     A__				,	A__				,	A__				,	A__	)							->							List[str]:
    """simple docstring"""

    snake_case				  =      original_name.split("."	)[0]
    snake_case				  =      key.split("."	)
    snake_case				  =      int(key_list[key_list.index(A__	) - 2]	)
    snake_case				  =      int(key_list[key_list.index(A__	) - 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 		lowercase_    (     A__	)							->							Dict:
    """simple docstring"""

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



def 		lowercase_    (     )							->							List[str]:
    """simple docstring"""

    snake_case				  =      "http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case				  =      Image.open(requests.get(A__				,	stream=A__	).raw	)

    return image



@torch.no_grad()
def 		lowercase_    (     A__				,	A__				,	A__	)							->							Optional[Any]:
    """simple docstring"""

    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(A__				,	A__				,	repo_type="dataset"	)				,	"r"	)	)
    snake_case				  =      {int(A__	): 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=A__	)

    # Prepare image
    snake_case				  =      prepare_img()
    snake_case				  =      image_processor(images=A__				,	return_tensors="pt"	).pixel_values

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

    # load original state dict
    snake_case				  =      torch.load(A__				,	map_location=torch.device("cpu"	)	)

    # rename keys
    snake_case				  =      rename_keys(A__	)

    # create HuggingFace model and load state dict
    snake_case				  =      PoolFormerForImageClassification(A__	)
    model.load_state_dict(A__	)
    model.eval()

    # Define image processor
    snake_case				  =      PoolFormerImageProcessor(crop_pct=A__	)
    snake_case				  =      image_processor(images=prepare_img()				,	return_tensors="pt"	).pixel_values

    # forward pass
    snake_case				  =      model(A__	)
    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]				,	A__				,	atol=1e-2	)

    # finally, save model and image processor
    logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'	)
    Path(A__	).mkdir(exist_ok=A__	)
    model.save_pretrained(A__	)
    print(F'Saving image processor to {pytorch_dump_folder_path}'	)
    image_processor.save_pretrained(A__	)


if __name__ == "__main__":
   _A						      =				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						      =				parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








import os

# Precomputes a list of the 100 first triangular numbers
_A						      =				[int(0.5 * n * (n + 1)) for n in range(1, 1_01)]



def 		lowercase_    (     )							->							Tuple:
    """simple docstring"""

    snake_case				  =      os.path.dirname(os.path.realpath(A__	)	)
    snake_case				  =      os.path.join(A__				,	"words.txt"	)

    snake_case				  =      ""
    with open(A__	) as f:
        snake_case				  =      f.readline()

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


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

