


"""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"""


class snake_case      :







		def __init__(					self      :       Optional[int]    ,   a__      :       List[Any]    ,   a__      :       List[str]    ,   a__      :       Tuple		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			None
						_A					  =			None
						_A					  =			graph

						self._normalize_graph(a__    ,   a__		)
						_A					  =			len(a__		)
						_A					  =			None







		def  a_ (					self      :       str    ,   a__      :       List[str]    ,   a__      :       List[Any]		)       ->						Dict:





						'''simple docstring'''







						if sources is int:
										_A					  =			[sources]
						if sinks is int:
										_A					  =			[sinks]

						if len(a__		) == 0 or len(a__		) == 0:
										return

						_A					  =			sources[0]
						_A					  =			sinks[0]

						# make fake vertex if there are more
						# than one source or sink
						if len(a__		) > 1 or len(a__		) > 1:
										_A					  =			0
										for i in sources:
														max_input_flow += sum(self.graph[i]		)

										_A					  =			len(self.graph		) + 1
										for room in self.graph:
														room.insert(0    ,   0		)
										self.graph.insert(0    ,   [0] * size		)
										for i in sources:
														_A					  =			max_input_flow
										_A					  =			0

										_A					  =			len(self.graph		) + 1
										for room in self.graph:
														room.append(0		)
										self.graph.append([0] * size		)
										for i in sinks:
														_A					  =			max_input_flow
										_A					  =			size - 1







		def  a_ (					self      :       Optional[int]		)       ->						List[Any]:





						'''simple docstring'''







						if self.maximum_flow_algorithm is None:
										raise Exception("You need to set maximum flow algorithm before."		)
						if self.source_index is None or self.sink_index is None:
										return 0

						self.maximum_flow_algorithm.execute()
						return self.maximum_flow_algorithm.getMaximumFlow()







		def  a_ (					self      :       List[Any]    ,   a__      :       Optional[Any]		)       ->						str:





						'''simple docstring'''







						_A					  =			algorithm(self		)





class snake_case      :







		def __init__(					self      :       List[str]    ,   a__      :       List[str]		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			flow_network
						_A					  =			flow_network.verticesCount
						_A					  =			flow_network.sourceIndex
						_A					  =			flow_network.sinkIndex
						# it's just a reference, so you shouldn't change
						# it in your algorithms, use deep copy before doing that
						_A					  =			flow_network.graph
						_A					  =			False







		def  a_ (					self      :       Optional[Any]		)       ->						List[Any]:





						'''simple docstring'''







						if not self.executed:
										self._algorithm()
										_A					  =			True







		def  a_ (					self      :       Any		)       ->						int:





						'''simple docstring'''







						pass





class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       Optional[Any]    ,   a__      :       Dict		)       ->						List[str]:





						'''simple docstring'''







						super().__init__(a__		)
						# use this to save your result
						_A					  =			-1







		def  a_ (					self      :       Any		)       ->						List[str]:





						'''simple docstring'''







						if not self.executed:
										raise Exception("You should execute algorithm before using its result!"		)

						return self.maximum_flow





class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       Union[str, Any]    ,   a__      :       Union[str, Any]		)       ->						Dict:





						'''simple docstring'''







						super().__init__(a__		)

						_A					  =			[[0] * self.verticies_count for i in range(self.verticies_count		)]

						_A					  =			[0] * self.verticies_count
						_A					  =			[0] * self.verticies_count







		def  a_ (					self      :       Any		)       ->						Dict:





						'''simple docstring'''







						_A					  =			self.verticies_count

						# push some substance to graph
						for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]		):
										self.preflow[self.source_index][nextvertex_index] += bandwidth
										self.preflow[nextvertex_index][self.source_index] -= bandwidth
										self.excesses[nextvertex_index] += bandwidth

						# Relabel-to-front selection rule
						_A					  =			[
						    i
						    for i in range(self.verticies_count		)
						    if i != self.source_index and i != self.sink_index
						]

						# move through list
						_A					  =			0
						while i < len(a__		):
										_A					  =			vertices_list[i]
										_A					  =			self.heights[vertex_index]
										self.process_vertex(a__		)
										if self.heights[vertex_index] > previous_height:
														# if it was relabeled, swap elements
														# and start from 0 index
														vertices_list.insert(0    ,   vertices_list.pop(a__		)		)
														_A					  =			0
										else:
														i += 1

						_A					  =			sum(self.preflow[self.source_index]		)







		def  a_ (					self      :       Dict    ,   a__      :       Any		)       ->						Optional[int]:





						'''simple docstring'''







						while self.excesses[vertex_index] > 0:
										for neighbour_index in range(self.verticies_count		):
														# if it's neighbour and current vertex is higher
														if (
														    self.graph[vertex_index][neighbour_index]
														    - self.preflow[vertex_index][neighbour_index]
														    > 0
														    and self.heights[vertex_index] > self.heights[neighbour_index]
														):
																		self.push(a__    ,   a__		)

										self.relabel(a__		)







		def  a_ (					self      :       str    ,   a__      :       Optional[int]    ,   a__      :       List[Any]		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			min(
						    self.excesses[from_index]    ,   self.graph[from_index][to_index] - self.preflow[from_index][to_index]    ,   )
						self.preflow[from_index][to_index] += preflow_delta
						self.preflow[to_index][from_index] -= preflow_delta
						self.excesses[from_index] -= preflow_delta
						self.excesses[to_index] += preflow_delta







		def  a_ (					self      :       Any    ,   a__      :       Dict		)       ->						Any:





						'''simple docstring'''







						_A					  =			None
						for to_index in range(self.verticies_count		):
										if (
										    self.graph[vertex_index][to_index]
										    - self.preflow[vertex_index][to_index]
										    > 0
										) and (min_height is None or self.heights[to_index] < min_height):
														_A					  =			self.heights[to_index]

						if min_height is not None:
										_A					  =			min_height + 1


if __name__ == "__main__":
		a_       							=    [0]
		a_       							=    [3]
		# graph = [
		#     [0, 0, 4, 6, 0, 0],
		#     [0, 0, 5, 2, 0, 0],
		#     [0, 0, 0, 0, 4, 4],
		#     [0, 0, 0, 0, 6, 6],
		#     [0, 0, 0, 0, 0, 0],
		#     [0, 0, 0, 0, 0, 0],
		# ]
		a_       							=    [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

		# prepare our network
		a_       							=    FlowNetwork(graph, entrances, exits)
		# set algorithm
		flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
		# and calculate
		a_       							=    flow_network.find_maximum_flow()

		print(f'''maximum flow is {maximum_flow}''')