


'''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()
lowercase__				     =		logging.get_logger(__name__)






def __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: Tuple      ,  __lowerCamelCase		: Any				)      ->     int:







     '''simple docstring'''






     _a						=		original_name.split("."				)[0]
     _a						=		key.split("."				)
     _a						=		int(key_list[key_list.index(__lowerCamelCase				) - 2]				)
     _a						=		int(key_list[key_list.index(__lowerCamelCase				) - 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 __UpperCamelCase   (			__lowerCamelCase		: str				)      ->     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(__lowerCamelCase      ,  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(__lowerCamelCase      ,  __lowerCamelCase      ,  "mlp.fc1"      ,  "output.conv1"				)
          if "mlp.fc2" in key:
               _a						=		replace_key_with_offset(__lowerCamelCase      ,  __lowerCamelCase      ,  "mlp.fc2"      ,  "output.conv2"				)
          if "norm1" in key:
               _a						=		replace_key_with_offset(__lowerCamelCase      ,  __lowerCamelCase      ,  "norm1"      ,  "before_norm"				)
          if "norm2" in key:
               _a						=		replace_key_with_offset(__lowerCamelCase      ,  __lowerCamelCase      ,  "norm2"      ,  "after_norm"				)
          if "layer_scale_1" in key:
               _a						=		replace_key_with_offset(__lowerCamelCase      ,  __lowerCamelCase      ,  "layer_scale_1"      ,  "layer_scale_1"				)
          if "layer_scale_2" in key:
               _a						=		replace_key_with_offset(__lowerCamelCase      ,  __lowerCamelCase      ,  "layer_scale_2"      ,  "layer_scale_2"				)
          if "head" in key:
               _a						=		key.replace("head"      ,  "classifier"				)
          _a						=		value
     return new_state_dict






def __UpperCamelCase   (			)      ->     Any:







     '''simple docstring'''






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

     return image






@torch.no_grad()
def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: Dict      ,  __lowerCamelCase		: Tuple				)      ->     str:







     '''simple docstring'''






     _a						=		PoolFormerConfig()

     # set attributes based on model_name
     _a						=		"huggingface/label-files"
     _a						=		model_name[-3:]
     _a						=		1000
     _a						=		"imagenet-1k-id2label.json"
     _a						=		(1, 1000)

     # set config attributes
     _a						=		json.load(open(hf_hub_download(__lowerCamelCase      ,  __lowerCamelCase      ,  repo_type="dataset"				)      ,  "r"				)				)
     _a						=		{int(__lowerCamelCase				): 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=__lowerCamelCase				)

     # Prepare image
     _a						=		prepare_img()
     _a						=		image_processor(images=__lowerCamelCase      ,  return_tensors="pt"				).pixel_values

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

     # load original state dict
     _a						=		torch.load(__lowerCamelCase      ,  map_location=torch.device("cpu"				)				)

     # rename keys
     _a						=		rename_keys(__lowerCamelCase				)

     # create HuggingFace model and load state dict
     _a						=		PoolFormerForImageClassification(__lowerCamelCase				)
     model.load_state_dict(__lowerCamelCase				)
     model.eval()

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

     # forward pass
     _a						=		model(__lowerCamelCase				)
     _a						=		outputs.logits

     # define expected logit slices for different models
     if size == "s12":
          _a						=		torch.tensor([-0.30_45, -0.67_58, -0.48_69]				)
     elif size == "s24":
          _a						=		torch.tensor([0.44_02, -0.13_74, -0.80_45]				)
     elif size == "s36":
          _a						=		torch.tensor([-0.60_80, -0.51_33, -0.58_98]				)
     elif size == "m36":
          _a						=		torch.tensor([0.39_52, 0.22_63, -1.26_68]				)
     elif size == "m48":
          _a						=		torch.tensor([0.11_67, -0.06_56, -0.34_23]				)
     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__":
   lowercase__				     =		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."
   )
   lowercase__				     =		parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''convbert'''






     def __init__(			self     ,							__UpperCamelCase=30_522     ,							__UpperCamelCase=768     ,							__UpperCamelCase=12     ,							__UpperCamelCase=12     ,							__UpperCamelCase=3_072     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=512     ,							__UpperCamelCase=2     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1e-12     ,							__UpperCamelCase=1     ,							__UpperCamelCase=0     ,							__UpperCamelCase=2     ,							__UpperCamelCase=768     ,							__UpperCamelCase=2     ,							__UpperCamelCase=9     ,							__UpperCamelCase=1     ,							__UpperCamelCase=None     ,							**__UpperCamelCase     ,							)       ->    Optional[int]:
          super().__init__(
              pad_token_id=__UpperCamelCase     ,							bos_token_id=__UpperCamelCase     ,							eos_token_id=__UpperCamelCase     ,							**__UpperCamelCase     ,							)

          _a						=		vocab_size
          _a						=		hidden_size
          _a						=		num_hidden_layers
          _a						=		num_attention_heads
          _a						=		intermediate_size
          _a						=		hidden_act
          _a						=		hidden_dropout_prob
          _a						=		attention_probs_dropout_prob
          _a						=		max_position_embeddings
          _a						=		type_vocab_size
          _a						=		initializer_range
          _a						=		layer_norm_eps
          _a						=		embedding_size
          _a						=		head_ratio
          _a						=		conv_kernel_size
          _a						=		num_groups
          _a						=		classifier_dropout







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     @property
     def  a_					(			self		)       ->    Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a						=		{0: "batch", 1: "choice", 2: "sequence"}
          else:
               _a						=		{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("token_type_ids", dynamic_axis),
              ]		)

