


"""simple docstring"""


import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)






def      a__ (				__lowercase     ,			__lowercase=False   )	->	List[Any]:
				_A					  =			[]
				for i in range(config.num_hidden_layers   ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((f"""module.blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")   )
								rename_keys.append((f"""module.blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")   )
								rename_keys.append(
								    (f"""module.blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")   )
								rename_keys.append((f"""module.blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")   )
								rename_keys.append((f"""module.blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")   )
								rename_keys.append((f"""module.blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")   )
								rename_keys.append((f"""module.blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")   )
								rename_keys.append((f"""module.blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")   )
								rename_keys.append((f"""module.blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")   )
								rename_keys.append((f"""module.blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")   )

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ("module.cls_token", "vit.embeddings.cls_token"),
				        ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
				        ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
				        ("module.pos_embed", "vit.embeddings.position_embeddings"),
				    ]   )

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ("module.norm.weight", "layernorm.weight"),
								        ("module.norm.bias", "layernorm.bias"),
								    ]   )

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								_A					  =			[(pair[0], pair[1][4:]) if pair[1].startswith("vit"   ) else pair for pair in rename_keys]
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("norm.weight", "vit.layernorm.weight"),
								        ("norm.bias", "vit.layernorm.bias"),
								        ("head.weight", "classifier.weight"),
								        ("head.bias", "classifier.bias"),
								    ]   )

				return rename_keys






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase=False   )	->	List[Any]:
				for i in range(config.num_hidden_layers   ):
								if base_model:
												_A					  =			""
								else:
												_A					  =			"vit."
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								_A					  =			state_dict.pop(f"""module.blocks.{i}.attn.qkv.weight"""   )
								_A					  =			state_dict.pop(f"""module.blocks.{i}.attn.qkv.bias"""   )
								# next, add query, keys and values (in that order) to the state dict
								_A					  =			in_proj_weight[
								    : config.hidden_size, :
								]
								_A					  =			in_proj_bias[: config.hidden_size]
								_A					  =			in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_A					  =			in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								_A					  =			in_proj_weight[
								    -config.hidden_size :, :
								]
								_A					  =			in_proj_bias[-config.hidden_size :]






def      a__ (				__lowercase   )	->	List[Any]:
				_A					  =			["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(__lowercase     ,			__lowercase   )






def      a__ (				__lowercase   )	->	Union[str, Any]:
				# projection head is used in the self-supervised pre-training in MSN,
				# for downstream task it's not needed.
				_A					  =			[
				    "module.fc.fc1.weight",
				    "module.fc.fc1.bias",
				    "module.fc.bn1.weight",
				    "module.fc.bn1.bias",
				    "module.fc.bn1.running_mean",
				    "module.fc.bn1.running_var",
				    "module.fc.bn1.num_batches_tracked",
				    "module.fc.fc2.weight",
				    "module.fc.fc2.bias",
				    "module.fc.bn2.weight",
				    "module.fc.bn2.bias",
				    "module.fc.bn2.running_mean",
				    "module.fc.bn2.running_var",
				    "module.fc.bn2.num_batches_tracked",
				    "module.fc.fc3.weight",
				    "module.fc.fc3.bias",
				]
				for k in ignore_keys:
								state_dict.pop(__lowercase     ,			__lowercase   )






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	Dict:
				_A					  =			dct.pop(__lowercase   )
				_A					  =			val






def      a__ (				__lowercase     ,			__lowercase   )	->	Optional[Any]:
				_A					  =			ViTMSNConfig()
				_A					  =			1000

				_A					  =			"datasets/huggingface/label-files"
				_A					  =			"imagenet-1k-id2label.json"
				_A					  =			json.load(open(hf_hub_download(__lowercase     ,			__lowercase   )     ,			"r"   )   )
				_A					  =			{int(__lowercase   ): v for k, v in idalabel.items()}
				_A					  =			idalabel
				_A					  =			{v: k for k, v in idalabel.items()}

				if "s16" in checkpoint_url:
								_A					  =			384
								_A					  =			1536
								_A					  =			6
				elif "l16" in checkpoint_url:
								_A					  =			1024
								_A					  =			4096
								_A					  =			24
								_A					  =			16
								_A					  =			0.1
				elif "b4" in checkpoint_url:
								_A					  =			4
				elif "l7" in checkpoint_url:
								_A					  =			7
								_A					  =			1024
								_A					  =			4096
								_A					  =			24
								_A					  =			16
								_A					  =			0.1

				_A					  =			ViTMSNModel(__lowercase   )

				_A					  =			torch.hub.load_state_dict_from_url(__lowercase     ,			map_location="cpu"   )["target_encoder"]

				_A					  =			ViTImageProcessor(size=config.image_size   )

				remove_projection_head(__lowercase   )
				_A					  =			create_rename_keys(__lowercase     ,			base_model=__lowercase   )

				for src, dest in rename_keys:
								rename_key(__lowercase     ,			__lowercase     ,			__lowercase   )
				read_in_q_k_v(__lowercase     ,			__lowercase     ,			base_model=__lowercase   )

				model.load_state_dict(__lowercase   )
				model.eval()

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

				_A					  =			Image.open(requests.get(__lowercase     ,			stream=__lowercase   ).raw   )
				_A					  =			ViTImageProcessor(
				    size=config.image_size     ,			image_mean=__lowercase     ,			image_std=__lowercase   )
				_A					  =			image_processor(images=__lowercase     ,			return_tensors="pt"   )

				# forward pass
				torch.manual_seed(2   )
				_A					  =			model(**__lowercase   )
				_A					  =			outputs.last_hidden_state

				# The following Colab Notebook was used to generate these outputs:
				# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
				if "s16" in checkpoint_url:
								_A					  =			torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]   )
				elif "b16" in checkpoint_url:
								_A					  =			torch.tensor([[14.2_889, -18.9_045, 11.7_281]]   )
				elif "l16" in checkpoint_url:
								_A					  =			torch.tensor([[41.5_028, -22.8_681, 45.6_475]]   )
				elif "b4" in checkpoint_url:
								_A					  =			torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]   )
				else:
								_A					  =			torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]   )

				# verify logits
				assert torch.allclose(last_hidden_state[:, 0, :3]     ,			__lowercase     ,			atol=1E-4   )

				print(f"""Saving model 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 __name__ == "__main__":
		a_       							=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--checkpoint_url",
		    default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
		    type=str,
		    help="URL of the checkpoint you'd like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)

		a_       							=    parser.parse_args()
		convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



"""simple docstring"""


from __future__ import annotations






def      a__ (				__lowercase     ,			__lowercase   )	->	float:
				_A					  =			sorted(numsa + numsa   )
				_A  ,		_A					  =			divmod(len(__lowercase   )     ,			2   )
				if mod == 1:
								return all_numbers[div]
				else:
								return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		a_       							=    [float(x) for x in input("Enter the elements of first array: ").split()]
		a_       							=    [float(x) for x in input("Enter the elements of second array: ").split()]
		print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')