



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]							)	->       Any:
				_lowerCamelCase        =				SwinConfig(image_size=1_92							)

				if "base" in model_name:
								_lowerCamelCase        =				6
								_lowerCamelCase        =				1_28
								_lowerCamelCase        =				(2, 2, 18, 2)
								_lowerCamelCase        =				(4, 8, 16, 32)
				elif "large" in model_name:
								_lowerCamelCase        =				12
								_lowerCamelCase        =				1_92
								_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_(   lowercase_     :							Dict							)	->       List[str]:
				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_(   lowercase_     :							List[str]						,   lowercase_     :							Optional[Any]							)	->       Dict:
				for key in orig_state_dict.copy().keys():
								_lowerCamelCase        =				orig_state_dict.pop(lowercase_							)

								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_(   lowercase_     :							str						,   lowercase_     :							Optional[int]						,   lowercase_     :							str						,   lowercase_     :							Optional[int]							)	->       Union[str, Any]:
				_lowerCamelCase        =				torch.load(lowercase_						,   map_location='''cpu'''							)['''model''']

				_lowerCamelCase        =				get_swin_config(lowercase_							)
				_lowerCamelCase        =				SwinForMaskedImageModeling(lowercase_							)
				model.eval()

				_lowerCamelCase        =				convert_state_dict(lowercase_						,   lowercase_							)
				model.load_state_dict(lowercase_							)

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

				_lowerCamelCase        =				ViTImageProcessor(size={'''height''': 1_92, '''width''': 1_92}							)
				_lowerCamelCase        =				Image.open(requests.get(lowercase_						,   stream=lowercase_							).raw							)
				_lowerCamelCase        =				image_processor(images=lowercase_						,   return_tensors='''pt'''							)

				with torch.no_grad():
								_lowerCamelCase        =				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__":
			__SCREAMING_SNAKE_CASE  :						List[str]    							=      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  :						Any    							=      parser.parse_args()
			convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""
def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							str							)	->       bool:
				_lowerCamelCase        =				len(lowercase_							)
				_lowerCamelCase        =				len(lowercase_							)
				_lowerCamelCase        =				[[False for _ in range(m + 1							)] for _ in range(n + 1							)]
				_lowerCamelCase        =				True
				for i in range(lowercase_							):
								for j in range(m + 1							):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				_lowerCamelCase        =				True
																if a[i].islower():
																				_lowerCamelCase        =				True
				return dp[n][m]


if __name__ == "__main__":
			import doctest

			doctest.testmod()
