






"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification






def        lowerCAmelCase (      UpperCamelCase_:							List[Any]	)       ->							Optional[Any]:







  '''simple docstring'''







  _a							=	SwinvaConfig()
  _a							=	swinva_name.split("_"	)

  _a							=	name_split[1]
  if "to" in name_split[3]:
    _a							=	int(name_split[3][-3:]	)
  else:
    _a							=	int(name_split[3]	)
  if "to" in name_split[2]:
    _a							=	int(name_split[2][-2:]	)
  else:
    _a							=	int(name_split[2][6:]	)

  if model_size == "tiny":
    _a							=	96
    _a							=	(2, 2, 6, 2)
    _a							=	(3, 6, 12, 24)
  elif model_size == "small":
    _a							=	96
    _a							=	(2, 2, 18, 2)
    _a							=	(3, 6, 12, 24)
  elif model_size == "base":
    _a							=	128
    _a							=	(2, 2, 18, 2)
    _a							=	(4, 8, 16, 32)
  else:
    _a							=	192
    _a							=	(2, 2, 18, 2)
    _a							=	(6, 12, 24, 48)

  if "to" in swinva_name:
    _a							=	(12, 12, 12, 6)

  if ("22k" in swinva_name) and ("to" not in swinva_name):
    _a							=	21841
    _a							=	"huggingface/label-files"
    _a							=	"imagenet-22k-id2label.json"
    _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()}

  else:
    _a							=	1000
    _a							=	"huggingface/label-files"
    _a							=	"imagenet-1k-id2label.json"
    _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()}

  _a							=	img_size
  _a							=	num_classes
  _a							=	embed_dim
  _a							=	depths
  _a							=	num_heads
  _a							=	window_size

  return config






def        lowerCAmelCase (      UpperCamelCase_:							int	)       ->							List[Any]:







  '''simple docstring'''







  if "patch_embed.proj" in name:
    _a							=	name.replace("patch_embed.proj"     , "embeddings.patch_embeddings.projection"	)
  if "patch_embed.norm" in name:
    _a							=	name.replace("patch_embed.norm"     , "embeddings.norm"	)
  if "layers" in name:
    _a							=	"encoder." + name
  if "attn.proj" in name:
    _a							=	name.replace("attn.proj"     , "attention.output.dense"	)
  if "attn" in name:
    _a							=	name.replace("attn"     , "attention.self"	)
  if "norm1" in name:
    _a							=	name.replace("norm1"     , "layernorm_before"	)
  if "norm2" in name:
    _a							=	name.replace("norm2"     , "layernorm_after"	)
  if "mlp.fc1" in name:
    _a							=	name.replace("mlp.fc1"     , "intermediate.dense"	)
  if "mlp.fc2" in name:
    _a							=	name.replace("mlp.fc2"     , "output.dense"	)
  if "q_bias" in name:
    _a							=	name.replace("q_bias"     , "query.bias"	)
  if "k_bias" in name:
    _a							=	name.replace("k_bias"     , "key.bias"	)
  if "v_bias" in name:
    _a							=	name.replace("v_bias"     , "value.bias"	)
  if "cpb_mlp" in name:
    _a							=	name.replace("cpb_mlp"     , "continuous_position_bias_mlp"	)
  if name == "norm.weight":
    _a							=	"layernorm.weight"
  if name == "norm.bias":
    _a							=	"layernorm.bias"

  if "head" in name:
    _a							=	name.replace("head"     , "classifier"	)
  else:
    _a							=	"swinv2." + name

  return name






def        lowerCAmelCase (      UpperCamelCase_:							List[str]     , UpperCamelCase_:							Tuple	)       ->							Union[str, Any]:







  '''simple docstring'''







  for key in orig_state_dict.copy().keys():
    _a							=	orig_state_dict.pop(__lowerCAmelCase	)

    if "mask" in key:
      continue
    elif "qkv" in key:
      _a							=	key.split("."	)
      _a							=	int(key_split[1]	)
      _a							=	int(key_split[3]	)
      _a							=	model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        _a							=	val[:dim, :]
        _a							=	val[dim : dim * 2, :]
        _a							=	val[-dim:, :]
      else:
        _a							=	val[:dim]
        _a							=	val[
            dim : dim * 2
        ]
        _a							=	val[-dim:]
    else:
      _a							=	val

  return orig_state_dict






def        lowerCAmelCase (      UpperCamelCase_:							str     , UpperCamelCase_:							Any	)       ->							Optional[Any]:







  '''simple docstring'''







  _a							=	timm.create_model(__lowerCAmelCase     , pretrained=__lowerCAmelCase	)
  timm_model.eval()

  _a							=	get_swinva_config(__lowerCAmelCase	)
  _a							=	SwinvaForImageClassification(__lowerCAmelCase	)
  model.eval()

  _a							=	convert_state_dict(timm_model.state_dict()     , __lowerCAmelCase	)
  model.load_state_dict(__lowerCAmelCase	)

  _a							=	"http://images.cocodataset.org/val2017/000000039769.jpg"

  _a							=	AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"     , "-"	)	)	)
  _a							=	Image.open(requests.get(__lowerCAmelCase     , stream=__lowerCAmelCase	).raw	)
  _a							=	image_processor(images=__lowerCAmelCase     , return_tensors="pt"	)

  _a							=	timm_model(inputs["pixel_values"]	)
  _a							=	model(**__lowerCAmelCase	).logits

  assert torch.allclose(__lowerCAmelCase     , __lowerCAmelCase     , atol=1E-3	)

  print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''	)
  model.save_pretrained(__lowerCAmelCase	)

  print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
  image_processor.save_pretrained(__lowerCAmelCase	)

  model.push_to_hub(
      repo_path_or_name=Path(__lowerCAmelCase     , __lowerCAmelCase	)     , organization="nandwalritik"     , commit_message="Add model"     , )


if __name__ == "__main__":
     UpperCamelCase							       =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--swinv2_name""",
         default="""swinv2_tiny_patch4_window8_256""",
         type=str,
         help="""Name of the Swinv2 timm model you\'d like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
     )

     UpperCamelCase							       =    parser.parse_args()
     convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)










"""simple docstring"""




from ..utils import DummyObject, requires_backends




class    lowercase_    (metaclass=_UpperCAmelCase     ):
      A__	:		Any       			=				['''torch''', '''torchsde''']






      def __init__(    self     ,     *a_     ,     **a_	)	->Optional[int]:







        '''simple docstring'''


        requires_backends(self     ,     ["torch", "torchsde"]	)






      @classmethod
      def 			lowerCamelCase__    (    cls     ,     *a_     ,     **a_	)	->List[Any]:







        '''simple docstring'''


        requires_backends(cls     ,     ["torch", "torchsde"]	)






      @classmethod
      def 			lowerCamelCase__    (    cls     ,     *a_     ,     **a_	)	->str:







        '''simple docstring'''


        requires_backends(cls     ,     ["torch", "torchsde"]	)


