
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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			List[Any]						,  _lowerCamelCase :			Dict=False)					->    List[Any]:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[int]			   =					[]
							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"
														__UpperCamelCase	:   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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Tuple						,  _lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			int=False)					->    List[str]:






							'''simple docstring'''





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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int])					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   List[Any]			   =					["head.weight", "head.bias"]
							for k in ignore_keys:
														state_dict.pop(_lowerCamelCase						,  _lowerCamelCase)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any])					->    Dict:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					[
							    "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(_lowerCamelCase						,  _lowerCamelCase)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			Any						,  _lowerCamelCase :			List[Any])					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[int]			   =					dct.pop(_lowerCamelCase)
							__UpperCamelCase	:   Any			   =					val




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			Tuple)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					ViTMSNConfig()
							__UpperCamelCase	:   Tuple			   =					1_000

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

							if "s16" in checkpoint_url:
														__UpperCamelCase	:   str			   =					384
														__UpperCamelCase	:   Tuple			   =					1_536
														__UpperCamelCase	:   Any			   =					6
							elif "l16" in checkpoint_url:
														__UpperCamelCase	:   Optional[Any]			   =					1_024
														__UpperCamelCase	:   Optional[Any]			   =					4_096
														__UpperCamelCase	:   Union[str, Any]			   =					24
														__UpperCamelCase	:   List[Any]			   =					16
														__UpperCamelCase	:   List[Any]			   =					0.1
							elif "b4" in checkpoint_url:
														__UpperCamelCase	:   Any			   =					4
							elif "l7" in checkpoint_url:
														__UpperCamelCase	:   Optional[int]			   =					7
														__UpperCamelCase	:   Any			   =					1_024
														__UpperCamelCase	:   List[Any]			   =					4_096
														__UpperCamelCase	:   Optional[Any]			   =					24
														__UpperCamelCase	:   str			   =					16
														__UpperCamelCase	:   Union[str, Any]			   =					0.1

							__UpperCamelCase	:   int			   =					ViTMSNModel(_lowerCamelCase)

							__UpperCamelCase	:   Tuple			   =					torch.hub.load_state_dict_from_url(_lowerCamelCase						,  map_location="cpu")["target_encoder"]

							__UpperCamelCase	:   Any			   =					ViTImageProcessor(size=config.image_size)

							remove_projection_head(_lowerCamelCase)
							__UpperCamelCase	:   Dict			   =					create_rename_keys(_lowerCamelCase						,  base_model=_lowerCamelCase)

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

							model.load_state_dict(_lowerCamelCase)
							model.eval()

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

							__UpperCamelCase	:   Optional[int]			   =					Image.open(requests.get(_lowerCamelCase						,  stream=_lowerCamelCase).raw)
							__UpperCamelCase	:   Dict			   =					ViTImageProcessor(
							    size=config.image_size						,  image_mean=_lowerCamelCase						,  image_std=_lowerCamelCase)
							__UpperCamelCase	:   str			   =					image_processor(images=_lowerCamelCase						,  return_tensors="pt")

							# forward pass
							torch.manual_seed(2)
							__UpperCamelCase	:   List[str]			   =					model(**_lowerCamelCase)
							__UpperCamelCase	:   Optional[int]			   =					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:
														__UpperCamelCase	:   Tuple			   =					torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]])
							elif "b16" in checkpoint_url:
														__UpperCamelCase	:   int			   =					torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]])
							elif "l16" in checkpoint_url:
														__UpperCamelCase	:   List[Any]			   =					torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]])
							elif "b4" in checkpoint_url:
														__UpperCamelCase	:   Dict			   =					torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]])
							else:
														__UpperCamelCase	:   List[str]			   =					torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]])

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

							print(F'Saving model 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 __name__ == "__main__":
		lowercase  :					Tuple													=       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  :					int													=       parser.parse_args()
		convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:int						)		-> Dict:
								__UpperCamelCase	:   Union[str, Any]			   =					{}







	def    _lowerCamelCase				(					self		:str						)		-> None:
								print(self.vertex						)
								for i in self.vertex:
															print(a     ,   " -> "     ,   " -> ".join([str(a						) for j in self.vertex[i]]						)						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:int     ,   a		:int						)		-> None:
								# check if vertex is already present,
								if from_vertex in self.vertex:
															self.vertex[from_vertex].append(a						)
								else:
															# else make a new vertex
															__UpperCamelCase	:   Optional[Any]			   =					[to_vertex]







	def    _lowerCamelCase				(					self		:Tuple						)		-> None:
								# visited array for storing already visited nodes
								__UpperCamelCase	:   Dict			   =					[False] * len(self.vertex						)

								# call the recursive helper function
								for i in range(len(self.vertex						)						):
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)







	def    _lowerCamelCase				(					self		:Any     ,   a		:int     ,   a		:list						)		-> None:
								# mark start vertex as visited
								__UpperCamelCase	:   int			   =					True

								print(a     ,   end=" "						)

								# Recur for all the vertices that are adjacent to this node
								for i in self.vertex:
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)


if __name__ == "__main__":
		lowercase  :					Dict													=       Graph()
		g.add_edge(0, 1)
		g.add_edge(0, 2)
		g.add_edge(1, 2)
		g.add_edge(2, 0)
		g.add_edge(2, 3)
		g.add_edge(3, 3)

		g.print_graph()
		print('DFS:')
		g.dfs()

		# OUTPUT:
		# 0  ->  1 -> 2
		# 1  ->  2
		# 2  ->  0 -> 3
		# 3  ->  3
		# DFS:
		#  0 1 2 3