


'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Any      =							SwinConfig(image_size=192							)

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

     __A	:       int      =							window_size
     __A	:       str      =							embed_dim
     __A	:       int      =							depths
     __A	:       List[str]      =							num_heads

     return config


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if "encoder.mask_token" in name:
          __A	:       Any      =							name.replace("encoder.mask_token"       ,					"embeddings.mask_token"							)
     if "encoder.patch_embed.proj" in name:
          __A	:       int      =							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	:       List[str]      =							name.replace("attn.proj"       ,					"attention.output.dense"							)
     if "attn" in name:
          __A	:       Tuple      =							name.replace("attn"       ,					"attention.self"							)
     if "norm1" in name:
          __A	:       int      =							name.replace("norm1"       ,					"layernorm_before"							)
     if "norm2" in name:
          __A	:       Tuple      =							name.replace("norm2"       ,					"layernorm_after"							)
     if "mlp.fc1" in name:
          __A	:       Tuple      =							name.replace("mlp.fc1"       ,					"intermediate.dense"							)
     if "mlp.fc2" in name:
          __A	:       Tuple      =							name.replace("mlp.fc2"       ,					"output.dense"							)

     if name == "encoder.norm.weight":
          __A	:       Union[str, Any]      =							"layernorm.weight"
     if name == "encoder.norm.bias":
          __A	:       Dict      =							"layernorm.bias"

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

     return name


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     for key in orig_state_dict.copy().keys():
          __A	:       Optional[int]      =							orig_state_dict.pop(SCREAMING_SNAKE_CASE							)

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

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

     return orig_state_dict


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							torch.load(SCREAMING_SNAKE_CASE       ,					map_location="cpu"							)["model"]

     __A	:       Union[str, Any]      =							get_swin_config(SCREAMING_SNAKE_CASE							)
     __A	:       str      =							SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE							)
     model.eval()

     __A	:       Dict      =							convert_state_dict(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     model.load_state_dict(SCREAMING_SNAKE_CASE							)

     __A	:       Optional[Any]      =							"http://images.cocodataset.org/val2017/000000039769.jpg"

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

     with torch.no_grad():
          __A	:       str      =							model(**SCREAMING_SNAKE_CASE							).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(SCREAMING_SNAKE_CASE							)

          print(f"Saving image processor to {pytorch_dump_folder_path}"							)
          image_processor.save_pretrained(SCREAMING_SNAKE_CASE							)

     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__":
      _UpperCamelCase 							=  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."""
      )

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





'''simple docstring'''


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE = " "							):




     '''simple docstring'''





     __A	:       List[str]      =							[]

     __A	:       Tuple      =							0
     for index, char in enumerate(SCREAMING_SNAKE_CASE							):
          if char == separator:
               split_words.append(string[last_index:index]							)
               __A	:       Union[str, Any]      =							index + 1
          elif index + 1 == len(SCREAMING_SNAKE_CASE							):
               split_words.append(string[last_index : index + 1]							)
     return split_words


if __name__ == "__main__":
      from doctest import testmod

      testmod()

