
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  :					Tuple						    =							logging.get_logger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     str       	=      original_name.split("""."""				)[0]
       lowercase							:     Optional[int]       	=      key.split("""."""				)
       lowercase							:     Any       	=      int(key_list[key_list.index(_lowerCamelCase				) - 2]				)
       lowercase							:     Tuple       	=      int(key_list[key_list.index(_lowerCamelCase				) - 1]				)
       lowercase							:     Optional[int]       	=      orig_block_num - offset

       lowercase							:     Tuple       	=      key.replace(f"{orig_block_num}.{layer_num}.{original_name}"					,	f"block.{new_block_num}.{layer_num}.{new_name}"				)
       return key







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







def 						_snake_case(  ) ->    Dict:
       lowercase							:     Any       	=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:     List[str]       	=      Image.open(requests.get(_lowerCamelCase					,	stream=_lowerCamelCase				).raw				)

       return image







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Dict:
       lowercase							:     int       	=      PoolFormerConfig()

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

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

       # load image processor
       lowercase							:     List[str]       	=      PoolFormerImageProcessor(crop_pct=_lowerCamelCase				)

       # Prepare image
       lowercase							:     int       	=      prepare_img()
       lowercase							:     str       	=      image_processor(images=_lowerCamelCase					,	return_tensors="""pt"""				).pixel_values

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

       # load original state dict
       lowercase							:     List[Any]       	=      torch.load(_lowerCamelCase					,	map_location=torch.device("""cpu"""				)				)

       # rename keys
       lowercase							:     str       	=      rename_keys(_lowerCamelCase				)

       # create HuggingFace model and load state dict
       lowercase							:     Optional[int]       	=      PoolFormerForImageClassification(_lowerCamelCase				)
       model.load_state_dict(_lowerCamelCase				)
       model.eval()

       # Define image processor
       lowercase							:     str       	=      PoolFormerImageProcessor(crop_pct=_lowerCamelCase				)
       lowercase							:     Optional[Any]       	=      image_processor(images=prepare_img()					,	return_tensors="""pt"""				).pixel_values

       # forward pass
       lowercase							:     Union[str, Any]       	=      model(_lowerCamelCase				)
       lowercase							:     Dict       	=      outputs.logits

       # define expected logit slices for different models
       if size == "s12":
              lowercase							:     str       	=      torch.tensor([-0.3045, -0.6758, -0.4869]				)
       elif size == "s24":
              lowercase							:     Optional[Any]       	=      torch.tensor([0.4402, -0.1374, -0.8045]				)
       elif size == "s36":
              lowercase							:     Union[str, Any]       	=      torch.tensor([-0.6080, -0.5133, -0.5898]				)
       elif size == "m36":
              lowercase							:     str       	=      torch.tensor([0.3952, 0.2263, -1.2668]				)
       elif size == "m48":
              lowercase							:     Optional[int]       	=      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]					,	_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  :					int						    =							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  :					Tuple						    =							parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)

"""simple docstring"""




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


_a      :		List[Any]           =   logging.get_logger(__name__)

_a      :		Any           =   {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			int							=		"swinv2"

     _UpperCamelCase							:			List[str]							=		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(		self				,     a__=224				,     a__=4				,     a__=3				,     a__=96				,     a__=[2, 2, 6, 2]				,     a__=[3, 6, 12, 24]				,     a__=7				,     a__=4.0				,     a__=True				,     a__=0.0				,     a__=0.0				,     a__=0.1				,     a__="gelu"				,     a__=False				,     a__=0.0_2				,     a__=1e-5				,     a__=32				,     **a__				,     ):
       super().__init__(**a__  )

       _lowerCAmelCase						:       int  			=     image_size
       _lowerCAmelCase						:       Optional[Any]  			=     patch_size
       _lowerCAmelCase						:       Any  			=     num_channels
       _lowerCAmelCase						:       List[Any]  			=     embed_dim
       _lowerCAmelCase						:       Optional[int]  			=     depths
       _lowerCAmelCase						:       List[Any]  			=     len(a__  )
       _lowerCAmelCase						:       Any  			=     num_heads
       _lowerCAmelCase						:       Tuple  			=     window_size
       _lowerCAmelCase						:       Tuple  			=     mlp_ratio
       _lowerCAmelCase						:       Any  			=     qkv_bias
       _lowerCAmelCase						:       Optional[int]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Tuple  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       str  			=     drop_path_rate
       _lowerCAmelCase						:       List[str]  			=     hidden_act
       _lowerCAmelCase						:       List[str]  			=     use_absolute_embeddings
       _lowerCAmelCase						:       Optional[int]  			=     layer_norm_eps
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       _lowerCAmelCase						:       Any  			=     encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       _lowerCAmelCase						:       Tuple  			=     int(embed_dim * 2 ** (len(a__  ) - 1)  )
       _lowerCAmelCase						:       Tuple  			=     (0, 0, 0, 0)




