





'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    int:
			a__     = SwinConfig(image_size=192     )

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

			a__     = window_size
			a__     = embed_dim
			a__     = depths
			a__     = num_heads

			return config







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

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

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

			return name







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     Optional[int]     )   ->    Union[str, Any]:
			for key in orig_state_dict.copy().keys():
						a__     = orig_state_dict.pop(__lowerCamelCase     )

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

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

			return orig_state_dict







def    _lowerCamelCase  (__lowerCamelCase  :     int					,  __lowerCamelCase  :     Any					,  __lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     Dict     )   ->    Any:
			a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )["model"]

			a__     = get_swin_config(__lowerCamelCase     )
			a__     = SwinForMaskedImageModeling(__lowerCamelCase     )
			model.eval()

			a__     = convert_state_dict(__lowerCamelCase					,  __lowerCamelCase     )
			model.load_state_dict(__lowerCamelCase     )

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

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

			with torch.no_grad():
						a__     = 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__":
		lowerCAmelCase_   :   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."
		)

		lowerCAmelCase_   :   Dict       =					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  (__lowerCamelCase  :     int = 400_0000     )   ->    int:
			a__     = [0, 1]
			a__     = 0
			while fib[i] <= n:
						fib.append(fib[i] + fib[i + 1]     )
						if fib[i + 2] > n:
									break
						i += 1
			a__     = 0
			for j in range(len(__lowerCamelCase     ) - 1     ):
						if fib[j] % 2 == 0:
									total += fib[j]

			return total


if __name__ == "__main__":
		print(f"""{solution() = }""")



