



"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def   snake_case__     (	__lowerCamelCase       :					Union[str, Any]			):
			"""simple docstring"""
			lowerCamelCase__	:	List[Any]				=SwinConfig(image_size=192			)

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

			lowerCamelCase__	:	Any				=window_size
			lowerCamelCase__	:	List[Any]				=embed_dim
			lowerCamelCase__	:	Any				=depths
			lowerCamelCase__	:	Optional[int]				=num_heads

			return config






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

			if name == "encoder.norm.weight":
						lowerCamelCase__	:	int				='''layernorm.weight'''
			if name == "encoder.norm.bias":
						lowerCamelCase__	:	int				='''layernorm.bias'''

			if "decoder" in name:
						pass
			else:
						lowerCamelCase__	:	Union[str, Any]				='''swin.''' + name

			return name






def   snake_case__     (	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					str			):
			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						lowerCamelCase__	:	Optional[Any]				=orig_state_dict.pop(__lowerCamelCase			)

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

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

			return orig_state_dict






def   snake_case__     (	__lowerCamelCase       :					int							,	__lowerCamelCase       :					Optional[int]							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Optional[int]			):
			"""simple docstring"""
			lowerCamelCase__	:	Union[str, Any]				=torch.load(__lowerCamelCase							,	map_location='''cpu'''			)['''model''']

			lowerCamelCase__	:	int				=get_swin_config(__lowerCamelCase			)
			lowerCamelCase__	:	Tuple				=SwinForMaskedImageModeling(__lowerCamelCase			)
			model.eval()

			lowerCamelCase__	:	List[Any]				=convert_state_dict(__lowerCamelCase							,	__lowerCamelCase			)
			model.load_state_dict(__lowerCamelCase			)

			lowerCamelCase__	:	Optional[Any]				='''http://images.cocodataset.org/val2017/000000039769.jpg'''

			lowerCamelCase__	:	str				=ViTImageProcessor(size={'''height''': 192, '''width''': 192}			)
			lowerCamelCase__	:	List[Any]				=Image.open(requests.get(__lowerCamelCase							,	stream=__lowerCamelCase			).raw			)
			lowerCamelCase__	:	Optional[Any]				=image_processor(images=__lowerCamelCase							,	return_tensors='''pt'''			)

			with torch.no_grad():
						lowerCamelCase__	:	str				=model(**__lowerCamelCase			).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(__lowerCamelCase			)

						print(f'''Saving image processor to {pytorch_dump_folder_path}'''			)
						image_processor.save_pretrained(__lowerCamelCase			)

			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__":
		_lowercase				:				Union[str, 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."
		)

		_lowercase				:				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 ..utils import DummyObject, requires_backends

class  __SCREAMING_SNAKE_CASE			(   metaclass=lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							['onnx']



					def __init__( self							:						List[str],						*lowerCamelCase							:						Union[str, Any],						**lowerCamelCase							:						str  )->      Optional[int]:
								requires_backends(self,						['''onnx''']  )



					@classmethod
					def 	snake_case ( cls							:						List[str],						*lowerCamelCase							:						Any,						**lowerCamelCase							:						Union[str, Any]  )->      Optional[int]:
								requires_backends(cls,						['''onnx''']  )




					@classmethod
					def 	snake_case ( cls							:						Union[str, Any],						*lowerCamelCase							:						Tuple,						**lowerCamelCase							:						Tuple  )->      Optional[int]:
								requires_backends(cls,						['''onnx''']  )




