


'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def        UpperCamelCase_(				_A   :List[Any]  )->		Any:
			UpperCamelCase__					=       SwinConfig(image_size=1_92  )

			if "base" in model_name:
						UpperCamelCase__					=       6
						UpperCamelCase__					=       1_28
						UpperCamelCase__					=       (2, 2, 18, 2)
						UpperCamelCase__					=       (4, 8, 16, 32)
			elif "large" in model_name:
						UpperCamelCase__					=       12
						UpperCamelCase__					=       1_92
						UpperCamelCase__					=       (2, 2, 18, 2)
						UpperCamelCase__					=       (6, 12, 24, 48)
			else:
						raise ValueError("Model not supported, only supports base and large variants"  )

			UpperCamelCase__					=       window_size
			UpperCamelCase__					=       embed_dim
			UpperCamelCase__					=       depths
			UpperCamelCase__					=       num_heads

			return config
def        UpperCamelCase_(				_A   :Optional[Any]  )->		int:
			if "encoder.mask_token" in name:
						UpperCamelCase__					=       name.replace("encoder.mask_token"				,     "embeddings.mask_token"  )
			if "encoder.patch_embed.proj" in name:
						UpperCamelCase__					=       name.replace("encoder.patch_embed.proj"				,     "embeddings.patch_embeddings.projection"  )
			if "encoder.patch_embed.norm" in name:
						UpperCamelCase__					=       name.replace("encoder.patch_embed.norm"				,     "embeddings.norm"  )
			if "attn.proj" in name:
						UpperCamelCase__					=       name.replace("attn.proj"				,     "attention.output.dense"  )
			if "attn" in name:
						UpperCamelCase__					=       name.replace("attn"				,     "attention.self"  )
			if "norm1" in name:
						UpperCamelCase__					=       name.replace("norm1"				,     "layernorm_before"  )
			if "norm2" in name:
						UpperCamelCase__					=       name.replace("norm2"				,     "layernorm_after"  )
			if "mlp.fc1" in name:
						UpperCamelCase__					=       name.replace("mlp.fc1"				,     "intermediate.dense"  )
			if "mlp.fc2" in name:
						UpperCamelCase__					=       name.replace("mlp.fc2"				,     "output.dense"  )

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

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

			return name
def        UpperCamelCase_(				_A   :int				,     _A   :int  )->		Union[str, Any]:
			for key in orig_state_dict.copy().keys():
						UpperCamelCase__					=       orig_state_dict.pop(_A  )

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

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

			return orig_state_dict
def        UpperCamelCase_(				_A   :str				,     _A   :Union[str, Any]				,     _A   :Optional[int]				,     _A   :Dict  )->		Optional[Any]:
			UpperCamelCase__					=       torch.load(_A				,     map_location="cpu"  )["model"]

			UpperCamelCase__					=       get_swin_config(_A  )
			UpperCamelCase__					=       SwinForMaskedImageModeling(_A  )
			model.eval()

			UpperCamelCase__					=       convert_state_dict(_A				,     _A  )
			model.load_state_dict(_A  )

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

			UpperCamelCase__					=       ViTImageProcessor(size={"height": 1_92, "width": 1_92}  )
			UpperCamelCase__					=       Image.open(requests.get(_A				,     stream=_A  ).raw  )
			UpperCamelCase__					=       image_processor(images=_A				,     return_tensors="pt"  )

			with torch.no_grad():
						UpperCamelCase__					=       model(**_A  ).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(_A  )

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

			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)






def        UpperCamelCase_(				_A   :int				,     _A   :int  )->		str:
			if number < 0 or shift_amount < 0:
						raise ValueError("both inputs must be positive integers"  )

			UpperCamelCase__					=       str(bin(_A  )  )
			binary_number += "0" * shift_amount
			return binary_number
def        UpperCamelCase_(				_A   :int				,     _A   :int  )->		str:
			if number < 0 or shift_amount < 0:
						raise ValueError("both inputs must be positive integers"  )

			UpperCamelCase__					=       str(bin(_A  )  )[2:]
			if shift_amount >= len(_A  ):
						return "0b0"
			UpperCamelCase__					=       binary_number[: len(_A  ) - shift_amount]
			return "0b" + shifted_binary_number
def        UpperCamelCase_(				_A   :int				,     _A   :int  )->		str:
			if number >= 0:  # Get binary representation of positive number
						UpperCamelCase__					=       "0" + str(bin(_A  )  ).strip("-"  )[2:]
			else:  # Get binary (2's complement) representation of negative number
						UpperCamelCase__					=       len(bin(_A  )[3:]  )  # Find 2's complement of number
						UpperCamelCase__					=       bin(abs(_A  ) - (1 << binary_number_length)  )[3:]
						UpperCamelCase__					=       (
						    "1" + "0" * (binary_number_length - len(_A  )) + binary_number
						)

			if shift_amount >= len(_A  ):
						return "0b" + binary_number[0] * len(_A  )
			return (
			    "0b"
			    + binary_number[0] * shift_amount
			    + binary_number[: len(_A  ) - shift_amount]
			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()


