






import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification

def   snake_case (UpperCAmelCase__  )	->    Any:
  UpperCamelCase_: List[Any]   				=    SwinConfig()
  UpperCamelCase_: Optional[Any]   				=    swin_name.split('_'  )

  UpperCamelCase_: str   				=    name_split[1]
  UpperCamelCase_: Optional[int]   				=    int(name_split[4]  )
  UpperCamelCase_: Union[str, Any]   				=    int(name_split[3][-1]  )

  if model_size == "tiny":
    UpperCamelCase_: List[str]   				=    9_6
    UpperCamelCase_: Optional[int]   				=    (2, 2, 6, 2)
    UpperCamelCase_: int   				=    (3, 6, 1_2, 2_4)
  elif model_size == "small":
    UpperCamelCase_: Optional[int]   				=    9_6
    UpperCamelCase_: List[str]   				=    (2, 2, 1_8, 2)
    UpperCamelCase_: Union[str, Any]   				=    (3, 6, 1_2, 2_4)
  elif model_size == "base":
    UpperCamelCase_: str   				=    1_2_8
    UpperCamelCase_: Tuple   				=    (2, 2, 1_8, 2)
    UpperCamelCase_: Tuple   				=    (4, 8, 1_6, 3_2)
  else:
    UpperCamelCase_: str   				=    1_9_2
    UpperCamelCase_: Optional[int]   				=    (2, 2, 1_8, 2)
    UpperCamelCase_: Any   				=    (6, 1_2, 2_4, 4_8)

  if "in22k" in swin_name:
    UpperCamelCase_: str   				=    2_1_8_4_1
  else:
    UpperCamelCase_: int   				=    1_0_0_0
    UpperCamelCase_: Optional[Any]   				=    'huggingface/label-files'
    UpperCamelCase_: Tuple   				=    'imagenet-1k-id2label.json'
    UpperCamelCase_: List[str]   				=    json.load(open(hf_hub_download(UpperCAmelCase__     ,   UpperCAmelCase__     ,   repo_type='dataset'  )     ,   'r'  )  )
    UpperCamelCase_: Dict   				=    {int(UpperCAmelCase__  ): v for k, v in idalabel.items()}
    UpperCamelCase_: int   				=    idalabel
    UpperCamelCase_: Tuple   				=    {v: k for k, v in idalabel.items()}

  UpperCamelCase_: List[Any]   				=    img_size
  UpperCamelCase_: List[str]   				=    num_classes
  UpperCamelCase_: Any   				=    embed_dim
  UpperCamelCase_: int   				=    depths
  UpperCamelCase_: List[Any]   				=    num_heads
  UpperCamelCase_: List[Any]   				=    window_size

  return config

def   snake_case (UpperCAmelCase__  )	->    Optional[int]:
  if "patch_embed.proj" in name:
    UpperCamelCase_: Optional[int]   				=    name.replace('patch_embed.proj'     ,   'embeddings.patch_embeddings.projection'  )
  if "patch_embed.norm" in name:
    UpperCamelCase_: List[str]   				=    name.replace('patch_embed.norm'     ,   'embeddings.norm'  )
  if "layers" in name:
    UpperCamelCase_: int   				=    'encoder.' + name
  if "attn.proj" in name:
    UpperCamelCase_: Tuple   				=    name.replace('attn.proj'     ,   'attention.output.dense'  )
  if "attn" in name:
    UpperCamelCase_: List[Any]   				=    name.replace('attn'     ,   'attention.self'  )
  if "norm1" in name:
    UpperCamelCase_: Any   				=    name.replace('norm1'     ,   'layernorm_before'  )
  if "norm2" in name:
    UpperCamelCase_: Any   				=    name.replace('norm2'     ,   'layernorm_after'  )
  if "mlp.fc1" in name:
    UpperCamelCase_: Union[str, Any]   				=    name.replace('mlp.fc1'     ,   'intermediate.dense'  )
  if "mlp.fc2" in name:
    UpperCamelCase_: Any   				=    name.replace('mlp.fc2'     ,   'output.dense'  )

  if name == "norm.weight":
    UpperCamelCase_: Optional[Any]   				=    'layernorm.weight'
  if name == "norm.bias":
    UpperCamelCase_: Union[str, Any]   				=    'layernorm.bias'

  if "head" in name:
    UpperCamelCase_: Dict   				=    name.replace('head'     ,   'classifier'  )
  else:
    UpperCamelCase_: Union[str, Any]   				=    'swin.' + name

  return name

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Any:
  for key in orig_state_dict.copy().keys():
    UpperCamelCase_: Union[str, Any]   				=    orig_state_dict.pop(UpperCAmelCase__  )

    if "mask" in key:
      continue
    elif "qkv" in key:
      UpperCamelCase_: List[str]   				=    key.split('.'  )
      UpperCamelCase_: Optional[int]   				=    int(key_split[1]  )
      UpperCamelCase_: Optional[int]   				=    int(key_split[3]  )
      UpperCamelCase_: List[str]   				=    model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        UpperCamelCase_: Tuple   				=    val[:dim, :]
        UpperCamelCase_: Union[str, Any]   				=    val[
            dim : dim * 2, :
        ]
        UpperCamelCase_: List[str]   				=    val[-dim:, :]
      else:
        UpperCamelCase_: int   				=    val[
            :dim
        ]
        UpperCamelCase_: Dict   				=    val[
            dim : dim * 2
        ]
        UpperCamelCase_: List[Any]   				=    val[
            -dim:
        ]
    else:
      UpperCamelCase_: Tuple   				=    val

  return orig_state_dict

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Dict:
  UpperCamelCase_: List[Any]   				=    timm.create_model(UpperCAmelCase__     ,   pretrained=UpperCAmelCase__  )
  timm_model.eval()

  UpperCamelCase_: Dict   				=    get_swin_config(UpperCAmelCase__  )
  UpperCamelCase_: int   				=    SwinForImageClassification(UpperCAmelCase__  )
  model.eval()

  UpperCamelCase_: int   				=    convert_state_dict(timm_model.state_dict()     ,   UpperCAmelCase__  )
  model.load_state_dict(UpperCAmelCase__  )

  UpperCamelCase_: Any   				=    'http://images.cocodataset.org/val2017/000000039769.jpg'

  UpperCamelCase_: Optional[int]   				=    AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'     ,   '-'  )  )  )
  UpperCamelCase_: Union[str, Any]   				=    Image.open(requests.get(UpperCAmelCase__     ,   stream=UpperCAmelCase__  ).raw  )
  UpperCamelCase_: int   				=    image_processor(images=UpperCAmelCase__     ,   return_tensors='pt'  )

  UpperCamelCase_: Dict   				=    timm_model(inputs['pixel_values']  )
  UpperCamelCase_: Any   				=    model(**UpperCAmelCase__  ).logits

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

  print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''  )
  model.save_pretrained(UpperCAmelCase__  )

  print(F'''Saving image processor to {pytorch_dump_folder_path}'''  )
  image_processor.save_pretrained(UpperCAmelCase__  )


if __name__ == "__main__":
   A_		:		str		     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--swin_name',
       default='swin_tiny_patch4_window7_224',
       type=str,
       help='Name of the Swin 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.'
   )

   A_		:		int		     =			parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A_		:		List[str]		     =			'.'


if __name__ == "__main__":
   A_		:		Dict		     =			os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
   A_		:		Dict		     =			[]
   A_		:		Optional[Any]		     =			[]
   with open(doctest_file_path) as fp:
      for line in fp:
         A_		:		Tuple		     =			line.strip()
         A_		:		Any		     =			os.path.join(REPO_PATH, line)
         if not (os.path.isfile(path) or os.path.isdir(path)):
            non_existent_paths.append(line)
         all_paths.append(path)
   if len(non_existent_paths) > 0:
      A_		:		str		     =			'\n'.join(non_existent_paths)
      raise ValueError(F'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
   if all_paths != sorted(all_paths):
      raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')