


"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			SwinConfig(image_size=192      )

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

			__lowerCAmelCase				    =			window_size
			__lowerCAmelCase				    =			embed_dim
			__lowerCAmelCase				    =			depths
			__lowerCAmelCase				    =			num_heads

			return config


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




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

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

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

			return name


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			for key in orig_state_dict.copy().keys():
						__lowerCAmelCase				    =			orig_state_dict.pop(_UpperCamelCase      )

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

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

			return orig_state_dict


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location="cpu"      )["model"]

			__lowerCAmelCase				    =			get_swin_config(_UpperCamelCase      )
			__lowerCAmelCase				    =			SwinForMaskedImageModeling(_UpperCamelCase      )
			model.eval()

			__lowerCAmelCase				    =			convert_state_dict(_UpperCamelCase    , _UpperCamelCase      )
			model.load_state_dict(_UpperCamelCase      )

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

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

			with torch.no_grad():
						__lowerCAmelCase				    =			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__":
	A     :   int			    =			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."
	)

	A     :   Optional[int]			    =			parser.parse_args()
	convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       int(__SCREAMING_SNAKE_CASE							)
						if decimal in (0, 1):  # Exit cases for the recursion
												return str(__SCREAMING_SNAKE_CASE							)
						lowercase_						, lowercase_	:     List[str]													=       divmod(__SCREAMING_SNAKE_CASE      ,      2							)
						return binary_recursive(__SCREAMING_SNAKE_CASE							) + str(__SCREAMING_SNAKE_CASE							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						lowercase_	:     str													=       str(__SCREAMING_SNAKE_CASE							).strip()
						if not number:
												raise ValueError('''No input value was provided'''							)
						lowercase_	:     Optional[int]													=       '''-''' if number.startswith('''-'''							) else ''''''
						lowercase_	:     Union[str, Any]													=       number.lstrip('''-'''							)
						if not number.isnumeric():
												raise ValueError('''Input value is not an integer'''							)
						return F'''{negative}0b{binary_recursive(int(__SCREAMING_SNAKE_CASE							)							)}'''


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

							testmod()



