



'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Optional[Any]:
		UpperCAmelCase_						=				SwinConfig(image_size=192 )

		if "base" in model_name:
				UpperCAmelCase_						=				6
				UpperCAmelCase_						=				128
				UpperCAmelCase_						=				(2, 2, 18, 2)
				UpperCAmelCase_						=				(4, 8, 16, 32)
		elif "large" in model_name:
				UpperCAmelCase_						=				12
				UpperCAmelCase_						=				192
				UpperCAmelCase_						=				(2, 2, 18, 2)
				UpperCAmelCase_						=				(6, 12, 24, 48)
		else:
				raise ValueError("Model not supported, only supports base and large variants" )

		UpperCAmelCase_						=				window_size
		UpperCAmelCase_						=				embed_dim
		UpperCAmelCase_						=				depths
		UpperCAmelCase_						=				num_heads

		return config







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Optional[Any]:
		if "encoder.mask_token" in name:
				UpperCAmelCase_						=				name.replace("encoder.mask_token"			,     "embeddings.mask_token" )
		if "encoder.patch_embed.proj" in name:
				UpperCAmelCase_						=				name.replace("encoder.patch_embed.proj"			,     "embeddings.patch_embeddings.projection" )
		if "encoder.patch_embed.norm" in name:
				UpperCAmelCase_						=				name.replace("encoder.patch_embed.norm"			,     "embeddings.norm" )
		if "attn.proj" in name:
				UpperCAmelCase_						=				name.replace("attn.proj"			,     "attention.output.dense" )
		if "attn" in name:
				UpperCAmelCase_						=				name.replace("attn"			,     "attention.self" )
		if "norm1" in name:
				UpperCAmelCase_						=				name.replace("norm1"			,     "layernorm_before" )
		if "norm2" in name:
				UpperCAmelCase_						=				name.replace("norm2"			,     "layernorm_after" )
		if "mlp.fc1" in name:
				UpperCAmelCase_						=				name.replace("mlp.fc1"			,     "intermediate.dense" )
		if "mlp.fc2" in name:
				UpperCAmelCase_						=				name.replace("mlp.fc2"			,     "output.dense" )

		if name == "encoder.norm.weight":
				UpperCAmelCase_						=				"layernorm.weight"
		if name == "encoder.norm.bias":
				UpperCAmelCase_						=				"layernorm.bias"

		if "decoder" in name:
				pass
		else:
				UpperCAmelCase_						=				"swin." + name

		return name







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[str]:
		for key in orig_state_dict.copy().keys():
				UpperCAmelCase_						=				orig_state_dict.pop(__SCREAMING_SNAKE_CASE )

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

						if "weight" in key:
								UpperCAmelCase_						=				val[:dim, :]
								UpperCAmelCase_						=				val[
								    dim : dim * 2, :
								]
								UpperCAmelCase_						=				val[-dim:, :]
						else:
								UpperCAmelCase_						=				val[
								    :dim
								]
								UpperCAmelCase_						=				val[
								    dim : dim * 2
								]
								UpperCAmelCase_						=				val[
								    -dim:
								]
				else:
						UpperCAmelCase_						=				val

		return orig_state_dict







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Union[str, Any]:
		UpperCAmelCase_						=				torch.load(__SCREAMING_SNAKE_CASE			,     map_location="cpu" )["model"]

		UpperCAmelCase_						=				get_swin_config(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				SwinForMaskedImageModeling(__SCREAMING_SNAKE_CASE )
		model.eval()

		UpperCAmelCase_						=				convert_state_dict(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		model.load_state_dict(__SCREAMING_SNAKE_CASE )

		UpperCAmelCase_						=				"http://images.cocodataset.org/val2017/000000039769.jpg"

		UpperCAmelCase_						=				ViTImageProcessor(size={"height": 192, "width": 192} )
		UpperCAmelCase_						=				Image.open(requests.get(__SCREAMING_SNAKE_CASE			,     stream=__SCREAMING_SNAKE_CASE ).raw )
		UpperCAmelCase_						=				image_processor(images=__SCREAMING_SNAKE_CASE			,     return_tensors="pt" )

		with torch.no_grad():
				UpperCAmelCase_						=				model(**__SCREAMING_SNAKE_CASE ).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(__SCREAMING_SNAKE_CASE )

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

		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)






import heapq as hq
import math
from collections.abc import Iterator



class 	lowerCamelCase      :







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				str(id_     )
							UpperCAmelCase_						=				None
							UpperCAmelCase_						=				None
							UpperCAmelCase_						=				[]
							UpperCAmelCase_						=				{}  # {vertex:distance}



					def __lt__(			self    ,	lowerCAmelCase     ):
							return self.key < other.key



					def __repr__(			self     ):
							return self.id



					def    A__		(			self    ,	lowerCAmelCase     ):
							self.neighbors.append(lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				weight







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[int]:
		# add the neighbors:
		graph[a - 1].add_neighbor(graph[b - 1] )
		graph[b - 1].add_neighbor(graph[a - 1] )
		# add the edges:
		graph[a - 1].add_edge(graph[b - 1]			,     __SCREAMING_SNAKE_CASE )
		graph[b - 1].add_edge(graph[a - 1]			,     __SCREAMING_SNAKE_CASE )







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							list:
		UpperCAmelCase_						=				[]
		for u in graph:
				UpperCAmelCase_						=				math.inf
				UpperCAmelCase_						=				None
		UpperCAmelCase_						=				0
		UpperCAmelCase_						=				graph[:]
		while q:
				UpperCAmelCase_						=				min(__SCREAMING_SNAKE_CASE )
				q.remove(__SCREAMING_SNAKE_CASE )
				for v in u.neighbors:
						if (v in q) and (u.edges[v.id] < v.key):
								UpperCAmelCase_						=				u
								UpperCAmelCase_						=				u.edges[v.id]
		for i in range(1			,     len(__SCREAMING_SNAKE_CASE ) ):
				a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
		return a







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Iterator[tuple]:
		for u in graph:
				UpperCAmelCase_						=				math.inf
				UpperCAmelCase_						=				None
		UpperCAmelCase_						=				0

		UpperCAmelCase_						=				list(__SCREAMING_SNAKE_CASE )
		hq.heapify(__SCREAMING_SNAKE_CASE )

		while h:
				UpperCAmelCase_						=				hq.heappop(__SCREAMING_SNAKE_CASE )
				for v in u.neighbors:
						if (v in h) and (u.edges[v.id] < v.key):
								UpperCAmelCase_						=				u
								UpperCAmelCase_						=				u.edges[v.id]
								hq.heapify(__SCREAMING_SNAKE_CASE )

		for i in range(1			,     len(__SCREAMING_SNAKE_CASE ) ):
				yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)







def snake_case__  (			)		->							None:
		pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()

