





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def _A       (   __magic_name__	):
   lowercase__			       =  SwinConfig(image_size=192	)

   if "base" in model_name:
      lowercase__			       =  6
      lowercase__			       =  128
      lowercase__			       =  (2, 2, 18, 2)
      lowercase__			       =  (4, 8, 16, 32)
   elif "large" in model_name:
      lowercase__			       =  12
      lowercase__			       =  192
      lowercase__			       =  (2, 2, 18, 2)
      lowercase__			       =  (6, 12, 24, 48)
   else:
      raise ValueError("Model not supported, only supports base and large variants"	)

   lowercase__			       =  window_size
   lowercase__			       =  embed_dim
   lowercase__			       =  depths
   lowercase__			       =  num_heads

   return config


def _A       (   __magic_name__	):
   if "encoder.mask_token" in name:
      lowercase__			       =  name.replace("encoder.mask_token"  , "embeddings.mask_token"	)
   if "encoder.patch_embed.proj" in name:
      lowercase__			       =  name.replace("encoder.patch_embed.proj"  , "embeddings.patch_embeddings.projection"	)
   if "encoder.patch_embed.norm" in name:
      lowercase__			       =  name.replace("encoder.patch_embed.norm"  , "embeddings.norm"	)
   if "attn.proj" in name:
      lowercase__			       =  name.replace("attn.proj"  , "attention.output.dense"	)
   if "attn" in name:
      lowercase__			       =  name.replace("attn"  , "attention.self"	)
   if "norm1" in name:
      lowercase__			       =  name.replace("norm1"  , "layernorm_before"	)
   if "norm2" in name:
      lowercase__			       =  name.replace("norm2"  , "layernorm_after"	)
   if "mlp.fc1" in name:
      lowercase__			       =  name.replace("mlp.fc1"  , "intermediate.dense"	)
   if "mlp.fc2" in name:
      lowercase__			       =  name.replace("mlp.fc2"  , "output.dense"	)

   if name == "encoder.norm.weight":
      lowercase__			       =  "layernorm.weight"
   if name == "encoder.norm.bias":
      lowercase__			       =  "layernorm.bias"

   if "decoder" in name:
      pass
   else:
      lowercase__			       =  "swin." + name

   return name


def _A       (   __magic_name__  , __magic_name__	):
   for key in orig_state_dict.copy().keys():
      lowercase__			       =  orig_state_dict.pop(__magic_name__	)

      if "attn_mask" in key:
         pass
      elif "qkv" in key:
         lowercase__			       =  key.split("."	)
         lowercase__			       =  int(key_split[2]	)
         lowercase__			       =  int(key_split[4]	)
         lowercase__			       =  model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

   return orig_state_dict


def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__	):
   lowercase__			       =  torch.load(__magic_name__  , map_location="cpu"	)["model"]

   lowercase__			       =  get_swin_config(__magic_name__	)
   lowercase__			       =  SwinForMaskedImageModeling(__magic_name__	)
   model.eval()

   lowercase__			       =  convert_state_dict(__magic_name__  , __magic_name__	)
   model.load_state_dict(__magic_name__	)

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

   lowercase__			       =  ViTImageProcessor(size={"height": 192, "width": 192}	)
   lowercase__			       =  Image.open(requests.get(__magic_name__  , stream=__magic_name__	).raw	)
   lowercase__			       =  image_processor(images=__magic_name__  , return_tensors="pt"	)

   with torch.no_grad():
      lowercase__			       =  model(**__magic_name__	).logits

   print(outputs.keys()	)
   print("Looks ok!"	)

   if pytorch_dump_folder_path is not None:
      print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''	)
      model.save_pretrained(__magic_name__	)

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

   if push_to_hub:
      print(f'''Pushing model and image processor for {model_name} to hub'''	)
      model.push_to_hub(f'''microsoft/{model_name}'''	)
      image_processor.push_to_hub(f'''microsoft/{model_name}'''	)


if __name__ == "__main__":
  _snake_case           =			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""swin-base-simmim-window6-192""",
      type=str,
      choices=["""swin-base-simmim-window6-192""", """swin-large-simmim-window12-192"""],
      help="""Name of the Swin SimMIM model you'd like to convert.""",
  )
  parser.add_argument(
      """--checkpoint_path""",
      default="""/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth""",
      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 output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

  _snake_case           =			parser.parse_args()
  convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)













from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike





class  lowerCAmelCase							(       lowercase_ ):







  def __init__(      self  :str   ,							_lowercase  :Optional[NestedDataStructureLike[PathLike]] = None   ,							_lowercase  :Optional[NamedSplit] = None   ,							_lowercase  :Optional[Features] = None   ,							_lowercase  :str = None   ,							_lowercase  :bool = False   ,							_lowercase  :bool = False   ,							_lowercase  :Optional[int] = None   ,							**_lowercase  :Tuple   ,							):






     '''simple docstring'''





     lowercase__			       =  path_or_paths
     lowercase__			       =  split if split or isinstance(_lowercase   ,							_lowercase ) else "train"
     lowercase__			       =  features
     lowercase__			       =  cache_dir
     lowercase__			       =  keep_in_memory
     lowercase__			       =  streaming
     lowercase__			       =  num_proc
     lowercase__			       =  kwargs







  @abstractmethod
  def   UpperCAmelCase					(      self  :Any ):






     '''simple docstring'''





     pass





class  lowerCAmelCase							(       lowercase_ ):







  def __init__(      self  :List[Any]   ,							_lowercase  :Optional[Features] = None   ,							_lowercase  :str = None   ,							_lowercase  :bool = False   ,							_lowercase  :bool = False   ,							_lowercase  :Optional[int] = None   ,							**_lowercase  :Optional[int]   ,							):






     '''simple docstring'''





     lowercase__			       =  features
     lowercase__			       =  cache_dir
     lowercase__			       =  keep_in_memory
     lowercase__			       =  streaming
     lowercase__			       =  num_proc
     lowercase__			       =  kwargs







  @abstractmethod
  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     pass






