


'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def     _UpperCamelCase				(			UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__					:       str    							=							SwinConfig(image_size=192		)

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

				__magic_name__					:       Optional[Any]    							=							window_size
				__magic_name__					:       Union[str, Any]    							=							embed_dim
				__magic_name__					:       int    							=							depths
				__magic_name__					:       int    							=							num_heads

				return config



def     _UpperCamelCase				(			UpperCamelCase__		):






				"""simple docstring"""



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

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

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

				return name



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



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

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

												if "weight" in key:
																__magic_name__					:       List[str]    							=							val[:dim, :]
																__magic_name__					:       Any    							=							val[
																    dim : dim * 2, :
																]
																__magic_name__					:       str    							=							val[-dim:, :]
												else:
																__magic_name__					:       List[Any]    							=							val[
																    :dim
																]
																__magic_name__					:       List[Any]    							=							val[
																    dim : dim * 2
																]
																__magic_name__					:       Optional[int]    							=							val[
																    -dim:
																]
								else:
												__magic_name__					:       List[str]    							=							val

				return orig_state_dict



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__					:       List[Any]    							=							torch.load(UpperCamelCase__  , map_location="cpu"		)["model"]

				__magic_name__					:       List[str]    							=							get_swin_config(UpperCamelCase__		)
				__magic_name__					:       Tuple    							=							SwinForMaskedImageModeling(UpperCamelCase__		)
				model.eval()

				__magic_name__					:       Any    							=							convert_state_dict(UpperCamelCase__  , UpperCamelCase__		)
				model.load_state_dict(UpperCamelCase__		)

				__magic_name__					:       Union[str, Any]    							=							"http://images.cocodataset.org/val2017/000000039769.jpg"

				__magic_name__					:       Union[str, Any]    							=							ViTImageProcessor(size={"height": 192, "width": 192}		)
				__magic_name__					:       Optional[int]    							=							Image.open(requests.get(UpperCamelCase__  , stream=UpperCamelCase__		).raw		)
				__magic_name__					:       str    							=							image_processor(images=UpperCamelCase__  , return_tensors="pt"		)

				with torch.no_grad():
								__magic_name__					:       List[Any]    							=							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							: str										=			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							: List[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 ..utils import DummyObject, requires_backends




class 					UpperCAmelCase	(     metaclass=snake_case_  ):
      _lowercase:     List[Any]			=  ['''torch''', '''scipy''']




      def __init__(     self     :					Tuple     ,     *__snake_case     :					Dict     ,     **__snake_case     :					List[str]    )							-> str:
            requires_backends(self     ,     ["""torch""", """scipy"""]    )




      @classmethod
      def 							lowercase__       (     cls     :					str     ,     *__snake_case     :					List[str]     ,     **__snake_case     :					str    )							-> Tuple:
            requires_backends(cls     ,     ["""torch""", """scipy"""]    )




      @classmethod
      def 							lowercase__       (     cls     :					Optional[int]     ,     *__snake_case     :					int     ,     **__snake_case     :					Union[str, Any]    )							-> Optional[int]:
            requires_backends(cls     ,     ["""torch""", """scipy"""]    )






