





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def    _A	(   lowerCamelCase ):
   a__						:    Dict   		=	SwinConfig(image_size=192 )

   if "base" in model_name:
      a__						:    Optional[int]   		=	6
      a__						:    Union[str, Any]   		=	128
      a__						:    Optional[Any]   		=	(2, 2, 18, 2)
      a__						:    List[Any]   		=	(4, 8, 16, 32)
   elif "large" in model_name:
      a__						:    Union[str, Any]   		=	12
      a__						:    Tuple   		=	192
      a__						:    List[str]   		=	(2, 2, 18, 2)
      a__						:    Optional[int]   		=	(6, 12, 24, 48)
   else:
      raise ValueError("Model not supported, only supports base and large variants" )

   a__						:    Optional[int]   		=	window_size
   a__						:    str   		=	embed_dim
   a__						:    int   		=	depths
   a__						:    Dict   		=	num_heads

   return config






def    _A	(   lowerCamelCase ):




   if "encoder.mask_token" in name:
      a__						:    Dict   		=	name.replace("encoder.mask_token"				,			"embeddings.mask_token" )
   if "encoder.patch_embed.proj" in name:
      a__						:    List[str]   		=	name.replace("encoder.patch_embed.proj"				,			"embeddings.patch_embeddings.projection" )
   if "encoder.patch_embed.norm" in name:
      a__						:    Dict   		=	name.replace("encoder.patch_embed.norm"				,			"embeddings.norm" )
   if "attn.proj" in name:
      a__						:    Union[str, Any]   		=	name.replace("attn.proj"				,			"attention.output.dense" )
   if "attn" in name:
      a__						:    Any   		=	name.replace("attn"				,			"attention.self" )
   if "norm1" in name:
      a__						:    List[Any]   		=	name.replace("norm1"				,			"layernorm_before" )
   if "norm2" in name:
      a__						:    Optional[Any]   		=	name.replace("norm2"				,			"layernorm_after" )
   if "mlp.fc1" in name:
      a__						:    Optional[int]   		=	name.replace("mlp.fc1"				,			"intermediate.dense" )
   if "mlp.fc2" in name:
      a__						:    Any   		=	name.replace("mlp.fc2"				,			"output.dense" )

   if name == "encoder.norm.weight":
      a__						:    Any   		=	"layernorm.weight"
   if name == "encoder.norm.bias":
      a__						:    Any   		=	"layernorm.bias"

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

   return name






def    _A	(   lowerCamelCase				,			lowerCamelCase ):




   for key in orig_state_dict.copy().keys():
      a__						:    Union[str, Any]   		=	orig_state_dict.pop(_UpperCamelCase )

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

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

   return orig_state_dict






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   a__						:    Any   		=	torch.load(_UpperCamelCase				,			map_location="cpu" )["model"]

   a__						:    Optional[Any]   		=	get_swin_config(_UpperCamelCase )
   a__						:    Tuple   		=	SwinForMaskedImageModeling(_UpperCamelCase )
   model.eval()

   a__						:    List[Any]   		=	convert_state_dict(_UpperCamelCase				,			_UpperCamelCase )
   model.load_state_dict(_UpperCamelCase )

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

   a__						:    Dict   		=	ViTImageProcessor(size={"height": 192, "width": 192} )
   a__						:    int   		=	Image.open(requests.get(_UpperCamelCase				,			stream=_UpperCamelCase ).raw )
   a__						:    Any   		=	image_processor(images=_UpperCamelCase				,			return_tensors="pt" )

   with torch.no_grad():
      a__						:    Optional[int]   		=	model(**_UpperCamelCase ).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(_UpperCamelCase )

      print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
      image_processor.save_pretrained(_UpperCamelCase )

   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__":
  SCREAMING_SNAKE_CASE__							:      Any         =    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."""
  )

  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    parser.parse_args()
  convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''





from typing import Any

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          _snake_case				= data
          _snake_case				= None







class   _a							:






     def __init__(					self       )				->     List[Any]:
          _snake_case				= None






     def 							_lowercase      (					self       )				->     str:
          _snake_case				= self.head
          while temp is not None:
               print(temp.data   ,end=" "       )
               _snake_case				= temp.next
          print()






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          _snake_case				= Node(_SCREAMING_SNAKE_CASE       )
          _snake_case				= self.head
          _snake_case				= new_node






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Tuple:
          if node_data_a == node_data_a:
               return
          else:
               _snake_case				= self.head
               while node_a is not None and node_a.data != node_data_a:
                    _snake_case				= node_a.next

               _snake_case				= self.head
               while node_a is not None and node_a.data != node_data_a:
                    _snake_case				= node_a.next

               if node_a is None or node_a is None:
                    return

               _snake_case				,   _snake_case				= node_a.data, node_a.data


if __name__ == "__main__":
   UpperCamelCase_		:     str          =		LinkedList()
   for i in range(5, 0, -1):
      ll.push(i)

   ll.print_list()

   ll.swap_nodes(1, 4)
   print('''After swapping''')
   ll.print_list()


