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()
__snake_case						=			logging.get_logger(__name__)





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






      '''simple docstring'''





      UpperCAmelCase			:       Optional[Any]			     =original_name.split('''.'''  )[0]
      UpperCAmelCase			:       int			     =key.split('''.'''  )
      UpperCAmelCase			:       List[str]			     =int(key_list[key_list.index(_UpperCamelCase  ) - 2]  )
      UpperCAmelCase			:       Tuple			     =int(key_list[key_list.index(_UpperCamelCase  ) - 1]  )
      UpperCAmelCase			:       Optional[Any]			     =orig_block_num - offset

      UpperCAmelCase			:       List[str]			     =key.replace(f'''{orig_block_num}.{layer_num}.{original_name}'''						,    f'''block.{new_block_num}.{layer_num}.{new_name}'''  )
      return key





def      lowerCAmelCase_       ( __lowerCAmelCase  )->     int:






      '''simple docstring'''





      UpperCAmelCase			:       Dict			     =OrderedDict()
      UpperCAmelCase			:       Dict			     =0, 0
      for key, value in state_dict.items():
            if key.startswith('''network'''  ):
                  UpperCAmelCase			:       Tuple			     =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			:       List[Any]			     =key[: key.find('''proj'''  )]
                  UpperCAmelCase			:       int			     =key.replace(_UpperCamelCase						,    f'''patch_embeddings.{total_embed_found}.'''  )
                  UpperCAmelCase			:       str			     =key.replace('''proj'''						,    '''projection'''  )
                  if key.endswith('''bias'''  ):
                        total_embed_found += 1
            if "patch_embeddings" in key:
                  UpperCAmelCase			:       List[Any]			     ='''poolformer.encoder.''' + key
            if "mlp.fc1" in key:
                  UpperCAmelCase			:       str			     =replace_key_with_offset(_UpperCamelCase						,    _UpperCamelCase						,    '''mlp.fc1'''						,    '''output.conv1'''  )
            if "mlp.fc2" in key:
                  UpperCAmelCase			:       Optional[Any]			     =replace_key_with_offset(_UpperCamelCase						,    _UpperCamelCase						,    '''mlp.fc2'''						,    '''output.conv2'''  )
            if "norm1" in key:
                  UpperCAmelCase			:       Dict			     =replace_key_with_offset(_UpperCamelCase						,    _UpperCamelCase						,    '''norm1'''						,    '''before_norm'''  )
            if "norm2" in key:
                  UpperCAmelCase			:       Union[str, Any]			     =replace_key_with_offset(_UpperCamelCase						,    _UpperCamelCase						,    '''norm2'''						,    '''after_norm'''  )
            if "layer_scale_1" in key:
                  UpperCAmelCase			:       Dict			     =replace_key_with_offset(_UpperCamelCase						,    _UpperCamelCase						,    '''layer_scale_1'''						,    '''layer_scale_1'''  )
            if "layer_scale_2" in key:
                  UpperCAmelCase			:       List[Any]			     =replace_key_with_offset(_UpperCamelCase						,    _UpperCamelCase						,    '''layer_scale_2'''						,    '''layer_scale_2'''  )
            if "head" in key:
                  UpperCAmelCase			:       Optional[Any]			     =key.replace('''head'''						,    '''classifier'''  )
            UpperCAmelCase			:       List[str]			     =value
      return new_state_dict





def      lowerCAmelCase_       ( )->     Optional[int]:






      '''simple docstring'''





      UpperCAmelCase			:       Optional[int]			     ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCAmelCase			:       List[Any]			     =Image.open(requests.get(_UpperCamelCase						,    stream=_UpperCamelCase  ).raw  )

      return image





@torch.no_grad()
def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase  )->     Optional[int]:






      '''simple docstring'''





      UpperCAmelCase			:       int			     =PoolFormerConfig()

      # set attributes based on model_name
      UpperCAmelCase			:       Union[str, Any]			     ='''huggingface/label-files'''
      UpperCAmelCase			:       Any			     =model_name[-3:]
      UpperCAmelCase			:       Any			     =10_00
      UpperCAmelCase			:       Optional[Any]			     ='''imagenet-1k-id2label.json'''
      UpperCAmelCase			:       Union[str, Any]			     =(1, 10_00)

      # set config attributes
      UpperCAmelCase			:       Optional[Any]			     =json.load(open(hf_hub_download(_UpperCamelCase						,    _UpperCamelCase						,    repo_type='''dataset'''  )						,    '''r'''  )  )
      UpperCAmelCase			:       Any			     ={int(_UpperCamelCase  ): v for k, v in idalabel.items()}
      UpperCAmelCase			:       int			     =idalabel
      UpperCAmelCase			:       Optional[int]			     ={v: k for k, v in idalabel.items()}
      if size == "s12":
            UpperCAmelCase			:       Optional[Any]			     =[2, 2, 6, 2]
            UpperCAmelCase			:       Optional[int]			     =[64, 1_28, 3_20, 5_12]
            UpperCAmelCase			:       Optional[int]			     =4.0
            UpperCAmelCase			:       Any			     =0.9
      elif size == "s24":
            UpperCAmelCase			:       List[Any]			     =[4, 4, 12, 4]
            UpperCAmelCase			:       str			     =[64, 1_28, 3_20, 5_12]
            UpperCAmelCase			:       Union[str, Any]			     =4.0
            UpperCAmelCase			:       Optional[Any]			     =0.9
      elif size == "s36":
            UpperCAmelCase			:       Optional[int]			     =[6, 6, 18, 6]
            UpperCAmelCase			:       List[str]			     =[64, 1_28, 3_20, 5_12]
            UpperCAmelCase			:       Dict			     =4.0
            UpperCAmelCase			:       Union[str, Any]			     =1e-6
            UpperCAmelCase			:       Union[str, Any]			     =0.9
      elif size == "m36":
            UpperCAmelCase			:       str			     =[6, 6, 18, 6]
            UpperCAmelCase			:       Dict			     =[96, 1_92, 3_84, 7_68]
            UpperCAmelCase			:       List[Any]			     =4.0
            UpperCAmelCase			:       List[Any]			     =1e-6
            UpperCAmelCase			:       str			     =0.95
      elif size == "m48":
            UpperCAmelCase			:       List[Any]			     =[8, 8, 24, 8]
            UpperCAmelCase			:       Union[str, Any]			     =[96, 1_92, 3_84, 7_68]
            UpperCAmelCase			:       Optional[int]			     =4.0
            UpperCAmelCase			:       Dict			     =1e-6
            UpperCAmelCase			:       Any			     =0.95
      else:
            raise ValueError(f'''Size {size} not supported'''  )

      # load image processor
      UpperCAmelCase			:       Optional[Any]			     =PoolFormerImageProcessor(crop_pct=_UpperCamelCase  )

      # Prepare image
      UpperCAmelCase			:       Tuple			     =prepare_img()
      UpperCAmelCase			:       str			     =image_processor(images=_UpperCamelCase						,    return_tensors='''pt'''  ).pixel_values

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

      # load original state dict
      UpperCAmelCase			:       List[Any]			     =torch.load(_UpperCamelCase						,    map_location=torch.device('''cpu'''  )  )

      # rename keys
      UpperCAmelCase			:       Optional[int]			     =rename_keys(_UpperCamelCase  )

      # create HuggingFace model and load state dict
      UpperCAmelCase			:       str			     =PoolFormerForImageClassification(_UpperCamelCase  )
      model.load_state_dict(_UpperCamelCase  )
      model.eval()

      # Define image processor
      UpperCAmelCase			:       str			     =PoolFormerImageProcessor(crop_pct=_UpperCamelCase  )
      UpperCAmelCase			:       Any			     =image_processor(images=prepare_img()						,    return_tensors='''pt'''  ).pixel_values

      # forward pass
      UpperCAmelCase			:       List[Any]			     =model(_UpperCamelCase  )
      UpperCAmelCase			:       Union[str, Any]			     =outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            UpperCAmelCase			:       List[str]			     =torch.tensor([-0.3045, -0.6758, -0.4869]  )
      elif size == "s24":
            UpperCAmelCase			:       Optional[int]			     =torch.tensor([0.4402, -0.1374, -0.8045]  )
      elif size == "s36":
            UpperCAmelCase			:       Dict			     =torch.tensor([-0.6080, -0.5133, -0.5898]  )
      elif size == "m36":
            UpperCAmelCase			:       Dict			     =torch.tensor([0.3952, 0.2263, -1.2668]  )
      elif size == "m48":
            UpperCAmelCase			:       Dict			     =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]						,    _UpperCamelCase						,    atol=1e-2  )

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


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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_    =				logging.get_logger(__name__)

lowerCAmelCase_    =				{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Any       			=      '''megatron-bert'''






			def __init__(self					,							__magic_name__=2_9056					,							__magic_name__=1024					,							__magic_name__=24					,							__magic_name__=16					,							__magic_name__=4096					,							__magic_name__="gelu"					,							__magic_name__=0.1					,							__magic_name__=0.1					,							__magic_name__=512					,							__magic_name__=2					,							__magic_name__=0.02					,							__magic_name__=1e-12					,							__magic_name__=0					,							__magic_name__="absolute"					,							__magic_name__=True					,							**__magic_name__					,							)     ->   Any:


							'''simple docstring'''



							super().__init__(pad_token_id=__magic_name__					,							**__magic_name__    )

							snake_case_						:    Union[str, Any]				=  vocab_size
							snake_case_						:    Dict				=  hidden_size
							snake_case_						:    Dict				=  num_hidden_layers
							snake_case_						:    Optional[int]				=  num_attention_heads
							snake_case_						:    int				=  hidden_act
							snake_case_						:    List[str]				=  intermediate_size
							snake_case_						:    Dict				=  hidden_dropout_prob
							snake_case_						:    str				=  attention_probs_dropout_prob
							snake_case_						:    str				=  max_position_embeddings
							snake_case_						:    Any				=  type_vocab_size
							snake_case_						:    int				=  initializer_range
							snake_case_						:    int				=  layer_norm_eps
							snake_case_						:    List[str]				=  position_embedding_type
							snake_case_						:    Dict				=  use_cache
