
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()
__magic_name__  							=	logging.get_logger(__name__)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    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				):
    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__     (   ):
    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				):
    snake_case__    =					PoolFormerConfig()

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

    # 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([-0.3_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]     ,		__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__":
     __magic_name__  							=	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.'''
     )
     __magic_name__  							=	parser.parse_args()
     convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__magic_name__  							=	TypeVar('''T''')




class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self		,						lowerCamelCase			):
          snake_case__    =					data
          snake_case__    =					None



      def __str__(      self			):
          return F"""{self.data}"""





class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self			):
          snake_case__    =					None
      def __iter__(      self			):
          snake_case__    =					self.top
          while node:
              yield node.data
              snake_case__    =					node.next
      def __str__(      self			):
          return "->".join([str(lowerCamelCase			) for item in self]			)
      def __len__(      self			):
          return len(tuple(iter(self			)			)			)
      def A_      (      self			):
          return self.top is None
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					Node(lowerCamelCase			)
          if not self.is_empty():
              snake_case__    =					self.top
          snake_case__    =					node
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("pop from empty stack"			)
          assert isinstance(self.top		,						lowerCamelCase			)
          snake_case__    =					self.top
          snake_case__    =					self.top.next
          return pop_node.data
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("peek from empty stack"			)

          assert self.top is not None
          return self.top.data



      def A_      (      self			):
          snake_case__    =					None


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

     testmod()



