import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any] )   ->       Union[str, Any]:
				_UpperCAmelCase       :		str 		=    SwinConfig(image_size=192 )

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

				_UpperCAmelCase       :		Union[str, Any] 		=    window_size
				_UpperCAmelCase       :		List[Any] 		=    embed_dim
				_UpperCAmelCase       :		Union[str, Any] 		=    depths
				_UpperCAmelCase       :		List[Any] 		=    num_heads

				return config

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Tuple )   ->       List[str]:




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

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

				if "decoder" in name:
								pass
				else:
								_UpperCAmelCase       :		Optional[int] 		=    '''swin.''' + name

				return name

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[Any]  ,      lowerCAmelCase:  List[Any] )   ->       Any:




				for key in orig_state_dict.copy().keys():
								_UpperCAmelCase       :		Tuple 		=    orig_state_dict.pop(__A )

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

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

				return orig_state_dict

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  List[Any] )   ->       int:
				_UpperCAmelCase       :		Any 		=    torch.load(__A  ,      map_location="cpu" )['''model''']

				_UpperCAmelCase       :		Optional[int] 		=    get_swin_config(__A )
				_UpperCAmelCase       :		Optional[Any] 		=    SwinForMaskedImageModeling(__A )
				model.eval()

				_UpperCAmelCase       :		Optional[Any] 		=    convert_state_dict(__A  ,      __A )
				model.load_state_dict(__A )

				_UpperCAmelCase       :		int 		=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''

				_UpperCAmelCase       :		Union[str, Any] 		=    ViTImageProcessor(size={"height": 192, "width": 192} )
				_UpperCAmelCase       :		Any 		=    Image.open(requests.get(__A  ,      stream=__A ).raw )
				_UpperCAmelCase       :		Any 		=    image_processor(images=__A  ,      return_tensors="pt" )

				with torch.no_grad():
								_UpperCAmelCase       :		Tuple 		=    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__":
					SCREAMING_SNAKE_CASE_        =      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.'
					)

					SCREAMING_SNAKE_CASE_        =      parser.parse_args()
					convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  str )   ->       int:
				if len(lowerCAmelCase ) != len(lowerCAmelCase ):
								raise ValueError("String lengths must match!" )

				_UpperCAmelCase       :		List[Any] 		=    0

				for chara, chara in zip(lowerCAmelCase  ,      lowerCAmelCase ):
								if chara != chara:
												count += 1

				return count


if __name__ == "__main__":
					import doctest

					doctest.testmod()


