import functools





def 	__lowerCamelCase							(UpperCAmelCase__     :							list[int]      , UpperCAmelCase__     :							list[int]  ):

					# Validation
					if not isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for day in days  ):
										raise ValueError("The parameter days should be a list of integers"  )

					if len(UpperCAmelCase__  ) != 3 or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for cost in costs  ):
										raise ValueError("The parameter costs should be a list of three integers"  )

					if len(UpperCAmelCase__  ) == 0:
										return 0

					if min(UpperCAmelCase__  ) <= 0:
										raise ValueError("All days elements should be greater than 0"  )

					if max(UpperCAmelCase__  ) >= 3_6_6:
										raise ValueError("All days elements should be less than 366"  )

					SCREAMING_SNAKE_CASE    			=       set(UpperCAmelCase__  )

					@functools.cache
					def dynamic_programming(UpperCAmelCase__     :							int  ) -> int:
										if index > 3_6_5:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1  )

										return min(
										    costs[0] + dynamic_programming(index + 1  )      , costs[1] + dynamic_programming(index + 7  )      , costs[2] + dynamic_programming(index + 3_0  )      , )

					return dynamic_programming(1  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





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





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple  ):
					SCREAMING_SNAKE_CASE    			=       ["head.weight", "head.bias"]
					for k in ignore_keys:
										state_dict.pop(UpperCAmelCase__      , UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							Any  ):
					# projection head is used in the self-supervised pre-training in MSN,
					# for downstream task it's not needed.
					SCREAMING_SNAKE_CASE    			=       [
					    "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(UpperCAmelCase__      , UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int      , UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							Dict  ):
					SCREAMING_SNAKE_CASE    			=       dct.pop(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       val





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							str  ):
					SCREAMING_SNAKE_CASE    			=       ViTMSNConfig()
					SCREAMING_SNAKE_CASE    			=       1_0_0_0

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

					if "s16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       3_8_4
										SCREAMING_SNAKE_CASE    			=       1_5_3_6
										SCREAMING_SNAKE_CASE    			=       6
					elif "l16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6
										SCREAMING_SNAKE_CASE    			=       0.1
					elif "b4" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       4
					elif "l7" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       7
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6
										SCREAMING_SNAKE_CASE    			=       0.1

					SCREAMING_SNAKE_CASE    			=       ViTMSNModel(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       torch.hub.load_state_dict_from_url(UpperCAmelCase__      , map_location="cpu"  )["target_encoder"]

					SCREAMING_SNAKE_CASE    			=       ViTImageProcessor(size=config.image_size  )

					remove_projection_head(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       create_rename_keys(UpperCAmelCase__      , base_model=UpperCAmelCase__  )

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

					model.load_state_dict(UpperCAmelCase__  )
					model.eval()

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

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

					# forward pass
					torch.manual_seed(2  )
					SCREAMING_SNAKE_CASE    			=       model(**UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       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:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[-1.0915, -1.4876, -1.1809]]  )
					elif "b16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[14.2889, -18.9045, 11.7281]]  )
					elif "l16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[41.5028, -22.8681, 45.6475]]  )
					elif "b4" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[-4.3868, 5.2932, -0.4137]]  )
					else:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[-0.1792, -0.6465, 2.4263]]  )

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

					print(F"Saving model to {pytorch_dump_folder_path}"  )
					model.save_pretrained(UpperCAmelCase__  )

					print(F"Saving image processor to {pytorch_dump_folder_path}"  )
					image_processor.save_pretrained(UpperCAmelCase__  )


if __name__ == "__main__":
							_lowerCamelCase					:   Optional[Any]							=		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.'''
							)

							_lowerCamelCase					:   Optional[Any]							=		parser.parse_args()
							convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




