



'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]  )      ->   Any:
							UpperCAmelCase :       Union[str, Any] 							=		SwinConfig(image_size=192  )

							if "base" in model_name:
														UpperCAmelCase :       List[str] 							=		6
														UpperCAmelCase :       int 							=		128
														UpperCAmelCase :       str 							=		(2, 2, 18, 2)
														UpperCAmelCase :       List[Any] 							=		(4, 8, 16, 32)
							elif "large" in model_name:
														UpperCAmelCase :       List[str] 							=		12
														UpperCAmelCase :       List[str] 							=		192
														UpperCAmelCase :       List[str] 							=		(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 :       Union[str, Any] 							=		embed_dim
							UpperCAmelCase :       Union[str, Any] 							=		depths
							UpperCAmelCase :       Tuple 							=		num_heads

							return config


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

							if name == "encoder.norm.weight":
														UpperCAmelCase :       List[Any] 							=		'''layernorm.weight'''
							if name == "encoder.norm.bias":
														UpperCAmelCase :       List[Any] 							=		'''layernorm.bias'''

							if "decoder" in name:
														pass
							else:
														UpperCAmelCase :       str 							=		'''swin.''' + name

							return name


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Tuple  )      ->   Dict:
							for key in orig_state_dict.copy().keys():
														UpperCAmelCase :       List[str] 							=		orig_state_dict.pop(_lowerCAmelCase  )

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

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

							return orig_state_dict


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						int  )      ->   int:
							UpperCAmelCase :       int 							=		torch.load(_lowerCAmelCase   ,      map_location='''cpu'''  )['''model''']

							UpperCAmelCase :       Dict 							=		get_swin_config(_lowerCAmelCase  )
							UpperCAmelCase :       Any 							=		SwinForMaskedImageModeling(_lowerCAmelCase  )
							model.eval()

							UpperCAmelCase :       str 							=		convert_state_dict(_lowerCAmelCase   ,      _lowerCAmelCase  )
							model.load_state_dict(_lowerCAmelCase  )

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

							UpperCAmelCase :       int 							=		ViTImageProcessor(size={'''height''': 192, '''width''': 192}  )
							UpperCAmelCase :       Any 							=		Image.open(requests.get(_lowerCAmelCase   ,      stream=_lowerCAmelCase  ).raw  )
							UpperCAmelCase :       int 							=		image_processor(images=_lowerCAmelCase   ,      return_tensors='''pt'''  )

							with torch.no_grad():
														UpperCAmelCase :       Dict 							=		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__":
						UpperCamelCase__:   List[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."
						)

						UpperCamelCase__:   Tuple								=			parser.parse_args()
						convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      List[Any]:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)
		if n == 0:
				return 0
		__a           =			float('''-inf'''			)
		for i in range(1    ,					n + 1			):
				__a           =			max(
				    lowerCAmelCase__    ,					prices[i - 1] + naive_cut_rod_recursive(n - i    ,					lowerCAmelCase__			)			)

		return max_revue

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      List[str]:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			[float('''-inf'''			) for _ in range(n + 1			)]
		return _top_down_cut_rod_recursive(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list    ,					lowerCAmelCase__ :     list			) ->      Union[str, Any]:
		if max_rev[n] >= 0:
				return max_rev[n]
		elif n == 0:
				return 0
		else:
				__a           =			float('''-inf'''			)
				for i in range(1    ,					n + 1			):
						__a           =			max(
						    lowerCAmelCase__    ,					prices[i - 1] + _top_down_cut_rod_recursive(n - i    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)    ,					)

				__a           =			max_revenue

		return max_rev[n]

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      Dict:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)

		# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
		# length 0.
		__a           =			[float('''-inf'''			) for _ in range(n + 1			)]
		__a           =			0

		for i in range(1    ,					n + 1			):
				__a           =			max_rev[i]
				for j in range(1    ,					i + 1			):
						__a           =			max(lowerCAmelCase__    ,					prices[j - 1] + max_rev[i - j]			)

				__a           =			max_revenue_i

		return max_rev[n]

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      str:
		if n < 0:
				__a           =			f'''n must be greater than or equal to 0. Got n = {n}'''
				raise ValueError(lowerCAmelCase__			)

		if n > len(lowerCAmelCase__			):
				__a           =			(
				    '''Each integral piece of rod must have a corresponding price. '''
				    f'''Got n = {n} but length of prices = {len(lowerCAmelCase__			)}'''
				)
				raise ValueError(lowerCAmelCase__			)

def        lowercase							(		) ->      int:
		__a           =			[6, 10, 12, 15, 20, 23]
		__a           =			len(lowerCAmelCase__			)

		# the best revenue comes from cutting the rod into 6 pieces, each
		# of length 1 resulting in a revenue of 6 * 6 = 36.
		__a           =			36

		__a           =			top_down_cut_rod(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			bottom_up_cut_rod(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			naive_cut_rod_recursive(lowerCAmelCase__    ,					lowerCAmelCase__			)

		assert expected_max_revenue == max_rev_top_down
		assert max_rev_top_down == max_rev_bottom_up
		assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
				main()






