






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def  a__ (__lowercase							:List[Any]   )		-> Dict:
				_A	:				int   =      SwinConfig(image_size=192   )

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

				_A	:				str   =      window_size
				_A	:				List[Any]   =      embed_dim
				_A	:				Tuple   =      depths
				_A	:				int   =      num_heads

				return config



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

				if name == "encoder.norm.weight":
								_A	:				Union[str, Any]   =      '''layernorm.weight'''
				if name == "encoder.norm.bias":
								_A	:				Optional[Any]   =      '''layernorm.bias'''

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

				return name



def  a__ (__lowercase							:str					,  __lowercase							:Union[str, Any]   )		-> int:
				for key in orig_state_dict.copy().keys():
								_A	:				List[str]   =      orig_state_dict.pop(__lowercase   )

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

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

				return orig_state_dict



def  a__ (__lowercase							:Union[str, Any]					,  __lowercase							:Dict					,  __lowercase							:Dict					,  __lowercase							:List[str]   )		-> Union[str, Any]:
				_A	:				Optional[Any]   =      torch.load(__lowercase					,  map_location='''cpu'''   )['''model''']

				_A	:				List[Any]   =      get_swin_config(__lowercase   )
				_A	:				Optional[int]   =      SwinForMaskedImageModeling(__lowercase   )
				model.eval()

				_A	:				List[Any]   =      convert_state_dict(__lowercase					,  __lowercase   )
				model.load_state_dict(__lowercase   )

				_A	:				List[Any]   =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

				with torch.no_grad():
								_A	:				int   =      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__":
	_UpperCamelCase			:	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.'
	)

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











from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_UpperCamelCase			:	List[str]								=HfArgumentParser(InitializationArguments)
_UpperCamelCase			:	Dict								=parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_UpperCamelCase			:	List[Any]								=AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_UpperCamelCase			:	str								={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
_UpperCamelCase			:	int								=AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_UpperCamelCase			:	List[str]								=AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)



