






"""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 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase=False       )						->    List[str]:

		"""simple docstring"""







		lowerCAmelCase_   :							Dict									=			[]
		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"
				lowerCAmelCase_   :							int									=			[(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 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase=False       )						->    int:

		"""simple docstring"""







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

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    Any:

		"""simple docstring"""







		lowerCAmelCase_   :							Union[str, Any]									=			["head.weight", "head.bias"]
		for k in ignore_keys:
				state_dict.pop(_lowercase      ,       _lowercase       )

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    Tuple:

		"""simple docstring"""







		lowerCAmelCase_   :							Optional[int]									=			[
		    "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 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase       )						->    List[Any]:

		"""simple docstring"""







		lowerCAmelCase_   :							Union[str, Any]									=			dct.pop(_lowercase       )
		lowerCAmelCase_   :							Optional[Any]									=			val

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase       )						->    List[str]:

		"""simple docstring"""







		lowerCAmelCase_   :							Tuple									=			ViTMSNConfig()
		lowerCAmelCase_   :							List[Any]									=			1000

		lowerCAmelCase_   :							str									=			"datasets/huggingface/label-files"
		lowerCAmelCase_   :							Optional[Any]									=			"imagenet-1k-id2label.json"
		lowerCAmelCase_   :							str									=			json.load(open(hf_hub_download(_lowercase      ,       _lowercase       )      ,       "r"       )       )
		lowerCAmelCase_   :							Optional[Any]									=			{int(_lowercase       ): v for k, v in idalabel.items()}
		lowerCAmelCase_   :							Dict									=			idalabel
		lowerCAmelCase_   :							Union[str, Any]									=			{v: k for k, v in idalabel.items()}

		if "s16" in checkpoint_url:
				lowerCAmelCase_   :							Any									=			384
				lowerCAmelCase_   :							Any									=			1536
				lowerCAmelCase_   :							Optional[int]									=			6
		elif "l16" in checkpoint_url:
				lowerCAmelCase_   :							str									=			1024
				lowerCAmelCase_   :							Any									=			4096
				lowerCAmelCase_   :							str									=			24
				lowerCAmelCase_   :							List[str]									=			16
				lowerCAmelCase_   :							Any									=			0.1
		elif "b4" in checkpoint_url:
				lowerCAmelCase_   :							Optional[Any]									=			4
		elif "l7" in checkpoint_url:
				lowerCAmelCase_   :							Dict									=			7
				lowerCAmelCase_   :							int									=			1024
				lowerCAmelCase_   :							List[str]									=			4096
				lowerCAmelCase_   :							Optional[int]									=			24
				lowerCAmelCase_   :							int									=			16
				lowerCAmelCase_   :							List[Any]									=			0.1

		lowerCAmelCase_   :							List[str]									=			ViTMSNModel(_lowercase       )

		lowerCAmelCase_   :							Union[str, Any]									=			torch.hub.load_state_dict_from_url(_lowercase      ,       map_location="cpu"       )["target_encoder"]

		lowerCAmelCase_   :							List[str]									=			ViTImageProcessor(size=config.image_size       )

		remove_projection_head(_lowercase       )
		lowerCAmelCase_   :							int									=			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()

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

		lowerCAmelCase_   :							Union[str, Any]									=			Image.open(requests.get(_lowercase      ,       stream=_lowercase       ).raw       )
		lowerCAmelCase_   :							List[Any]									=			ViTImageProcessor(
		    size=config.image_size      ,       image_mean=_lowercase      ,       image_std=_lowercase       )
		lowerCAmelCase_   :							List[Any]									=			image_processor(images=_lowercase      ,       return_tensors="pt"       )

		# forward pass
		torch.manual_seed(2       )
		lowerCAmelCase_   :							List[Any]									=			model(**_lowercase       )
		lowerCAmelCase_   :							Optional[Any]									=			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:
				lowerCAmelCase_   :							Dict									=			torch.tensor([[-1.09_15, -1.48_76, -1.18_09]]       )
		elif "b16" in checkpoint_url:
				lowerCAmelCase_   :							Union[str, Any]									=			torch.tensor([[14.28_89, -18.90_45, 11.72_81]]       )
		elif "l16" in checkpoint_url:
				lowerCAmelCase_   :							Any									=			torch.tensor([[41.50_28, -22.86_81, 45.64_75]]       )
		elif "b4" in checkpoint_url:
				lowerCAmelCase_   :							Optional[Any]									=			torch.tensor([[-4.38_68, 5.29_32, -0.41_37]]       )
		else:
				lowerCAmelCase_   :							Tuple									=			torch.tensor([[-0.17_92, -0.64_65, 2.42_63]]       )

		# 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__":
					lowercase__          = 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."""
					)

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







"""simple docstring"""




from __future__ import annotations






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list[int]    ,       _lowercase						:    int					) ->int:






	'''simple docstring'''






	if len(_lowercase					) < k or k < 0:
		raise ValueError("Invalid Input"					)
	a      :		Optional[Any]	=				sum(array[:k]					)
	for i in range(len(_lowercase					) - k					):
		a      :		Optional[Any]	=				current_sum - array[i] + array[i + k]
		a      :		Union[str, Any]	=				max(_lowercase    ,       _lowercase					)
	return max_sum


if __name__ == "__main__":
		from doctest import testmod
		from random import randint

		testmod()
		a						: Any     	=			[randint(-1000, 1000) for i in range(100)]
		a						: List[str]     	=			randint(0, 110)
		print(F'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')


