




"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[str]					=					SwinConfig(image_size=192	)

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

				_lowerCamelCase  :    Any					=					window_size
				_lowerCamelCase  :    Tuple					=					embed_dim
				_lowerCamelCase  :    Union[str, Any]					=					depths
				_lowerCamelCase  :    int					=					num_heads

				return config







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

				if name == "encoder.norm.weight":
								_lowerCamelCase  :    int					=					'layernorm.weight'
				if name == "encoder.norm.bias":
								_lowerCamelCase  :    Optional[Any]					=					'layernorm.bias'

				if "decoder" in name:
								pass
				else:
								_lowerCamelCase  :    Dict					=					'swin.' + name

				return name







def   _snake_case    (       lowercase__							,					lowercase__	):
				for key in orig_state_dict.copy().keys():
								_lowerCamelCase  :    Any					=					orig_state_dict.pop(lowercase__	)

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

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

				return orig_state_dict







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    str					=					torch.load(lowercase__							,					map_location='cpu'	)['model']

				_lowerCamelCase  :    int					=					get_swin_config(lowercase__	)
				_lowerCamelCase  :    Optional[Any]					=					SwinForMaskedImageModeling(lowercase__	)
				model.eval()

				_lowerCamelCase  :    str					=					convert_state_dict(lowercase__							,					lowercase__	)
				model.load_state_dict(lowercase__	)

				_lowerCamelCase  :    Any					=					'http://images.cocodataset.org/val2017/000000039769.jpg'

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

				with torch.no_grad():
								_lowerCamelCase  :    Tuple					=					model(**lowercase__	).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(lowercase__	)

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

				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__  		=		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__  		=		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    (       lowercase__	):
				return "".join(chr(ord(lowercase__	) - 32	) if 'a' <= char <= 'z' else char for char in word	)


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

			testmod()