



import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase				:					Tuple     					=logging.get_logger(__name__)
@dataclass
class lowerCAmelCase_       :







	'''simple docstring'''




	A_       :     nn.Module
	A_       :     List[nn.Module]									=		field(default_factory=A_			)
	A_       :     list									=		field(default_factory=A_			)

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  len(list(m.modules()       )       ) == 1 or isinstance(lowerCamelCase   ,       nn.Convad       ) or isinstance(lowerCamelCase   ,       nn.BatchNormad       )
				if has_not_submodules:
							if not isinstance(lowerCamelCase   ,       lowerCamelCase       ):
										self.traced.append(lowerCamelCase       )

	def __call__(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				for m in self.module.modules():
							self.handles.append(m.register_forward_hook(self._forward_hook       )       )
				self.module(lowerCamelCase       )
				[x.remove() for x in self.handles]
				return self







	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				return list(filter(lambda lowerCamelCase       : len(list(x.state_dict().keys()       )       ) > 0   ,       self.traced       )       )


@dataclass
class lowerCAmelCase_       :







	'''simple docstring'''




	A_       :     nn.Module
	A_       :     nn.Module
	A_       :     int									=		0
	A_       :     List									=		field(default_factory=A_			)
	A_       :     List									=		field(default_factory=A_			)

	def __call__(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  Tracker(self.dest       )(lowerCamelCase       ).parametrized
				a__					=  Tracker(self.src       )(lowerCamelCase       ).parametrized

				a__					=  list(filter(lambda lowerCamelCase       : type(lowerCamelCase       ) not in self.src_skip   ,       lowerCamelCase       )       )
				a__					=  list(filter(lambda lowerCamelCase       : type(lowerCamelCase       ) not in self.dest_skip   ,       lowerCamelCase       )       )

				if len(lowerCamelCase       ) != len(lowerCamelCase       ):
							raise Exception(
							    f'Numbers of operations are different. Source module has {len(lowerCamelCase       )} operations while'
							    f' destination module has {len(lowerCamelCase       )}.'       )

				for dest_m, src_m in zip(lowerCamelCase   ,       lowerCamelCase       ):
							dest_m.load_state_dict(src_m.state_dict()       )
							if self.verbose == 1:
										print(f'Transfered from={src_m} to={dest_m}'       )




def    UpperCAmelCase					(  lowercase__						:					nn.Module				,					lowercase__						:					nn.Module	):



			'''simple docstring'''





			a__					=  from_model.state_dict()
			a__					=  our_model.state_dict()
			a__					=  our_model.config
			a__					=  []
			for stage_idx in range(len(config.hidden_sizes	)	):
						for block_id in range(config.depths[stage_idx]	):
									a__					=  f'block{stage_idx + 1}.{block_id}.layer_scale_1'
									a__					=  f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

									all_keys.append((from_key, to_key)	)
									a__					=  f'block{stage_idx + 1}.{block_id}.layer_scale_2'
									a__					=  f'van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'

									all_keys.append((from_key, to_key)	)

			for from_key, to_key in all_keys:
						a__					=  from_state_dict.pop(lowercase__	)

			our_model.load_state_dict(lowercase__	)
			return our_model




def    UpperCAmelCase					(  lowercase__						:					str				,					lowercase__						:					VanConfig				,					lowercase__						:					str				,					lowercase__						:					nn.Module				,					lowercase__						:					Path				,					lowercase__						:					bool = True				,					):



			'''simple docstring'''





			print(f'Downloading weights for {name}...'	)
			a__					=  cached_download(lowercase__	)
			print(f'Converting {name}...'	)
			a__					=  torch.load(lowercase__	)["""state_dict"""]
			from_model.load_state_dict(lowercase__	)
			from_model.eval()
			with torch.no_grad():
						a__					=  VanForImageClassification(lowercase__	).eval()
						a__					=  ModuleTransfer(src=lowercase__				,					dest=lowercase__	)
						a__					=  torch.randn((1, 3, 224, 224)	)
						module_transfer(lowercase__	)
						a__					=  copy_parameters(lowercase__				,					lowercase__	)

			if not torch.allclose(from_model(lowercase__	)				,					our_model(lowercase__	).logits	):
						raise ValueError("""The model logits don't match the original one."""	)

			a__					=  name
			print(lowercase__	)

			if push_to_hub:
						our_model.push_to_hub(
						    repo_path_or_name=save_directory / checkpoint_name				,					commit_message="""Add model"""				,					use_temp_dir=lowercase__				,					)

						# we can use the convnext one
						a__					=  AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""	)
						image_processor.push_to_hub(
						    repo_path_or_name=save_directory / checkpoint_name				,					commit_message="""Add image processor"""				,					use_temp_dir=lowercase__				,					)

						print(f'Pushed {checkpoint_name}'	)




def    UpperCAmelCase					(  lowercase__						:					Path				,					lowercase__						:					str = None				,					lowercase__						:					bool = True	):



			'''simple docstring'''





			a__					=  """imagenet-1k-id2label.json"""
			a__					=  1000

			a__					=  """huggingface/label-files"""
			a__					=  num_labels
			a__					=  json.load(open(hf_hub_download(lowercase__				,					lowercase__				,					repo_type="""dataset"""	)				,					"""r"""	)	)
			a__					=  {int(lowercase__	): v for k, v in idalabel.items()}

			a__					=  idalabel
			a__					=  {v: k for k, v in idalabel.items()}

			a__					=  partial(lowercase__				,					num_labels=lowercase__				,					idalabel=lowercase__				,					labelaid=lowercase__	)

			a__					=  {
			    """van-tiny""": ImageNetPreTrainedConfig(
			        hidden_sizes=[32, 64, 160, 256]				,					depths=[3, 3, 5, 2]				,					mlp_ratios=[8, 8, 4, 4]				,					),
			    """van-small""": ImageNetPreTrainedConfig(
			        hidden_sizes=[64, 128, 320, 512]				,					depths=[2, 2, 4, 2]				,					mlp_ratios=[8, 8, 4, 4]				,					),
			    """van-base""": ImageNetPreTrainedConfig(
			        hidden_sizes=[64, 128, 320, 512]				,					depths=[3, 3, 12, 3]				,					mlp_ratios=[8, 8, 4, 4]				,					),
			    """van-large""": ImageNetPreTrainedConfig(
			        hidden_sizes=[64, 128, 320, 512]				,					depths=[3, 5, 27, 3]				,					mlp_ratios=[8, 8, 4, 4]				,					),
			}

			a__					=  {
			    """van-tiny""": van_tiny,
			    """van-small""": van_small,
			    """van-base""": van_base,
			    """van-large""": van_large,
			}

			a__					=  {
			    """van-tiny""": (
			        """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
			    ),
			    """van-small""": (
			        """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
			    ),
			    """van-base""": (
			        """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
			    ),
			    """van-large""": (
			        """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
			    ),
			}

			if model_name:
						convert_weight_and_push(
						    lowercase__				,					names_to_config[model_name]				,					checkpoint=names_to_original_checkpoints[model_name]				,					from_model=names_to_original_models[model_name]()				,					save_directory=lowercase__				,					push_to_hub=lowercase__				,					)
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(
									    lowercase__				,					lowercase__				,					checkpoint=names_to_original_checkpoints[model_name]				,					from_model=names_to_original_models[model_name]()				,					save_directory=lowercase__				,					push_to_hub=lowercase__				,					)


if __name__ == "__main__":
	_lowercase				:					List[Any]     					=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model-name""",
	    default=None,
	    type=str,
	    help=(
	        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
	        """ currently: van-tiny/small/base/large. If `None`, all of them will the converted."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default=None,
	    type=Path,
	    required=True,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument(
	    """--van_dir""",
	    required=True,
	    type=Path,
	    help=(
	        """A path to VAN's original implementation directory. You can download from here:"""
	        """ https://github.com/Visual-Attention-Network/VAN-Classification"""
	    ),
	)
	parser.add_argument(
	    """--push_to_hub""",
	    default=True,
	    type=bool,
	    required=False,
	    help="""If True, push model and image processor to the hub.""",
	)

	_lowercase				:					int     					=parser.parse_args()
	_lowercase				:					Path     					=args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	_lowercase				:					Optional[int]     					=args.van_dir
	# append the path to the parents to maskformer dir
	sys.path.append(str(van_dir.parent))
	from van.models.van import van_base, van_large, van_small, van_tiny

	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






def    UpperCAmelCase					(  lowercase__						:					List[Any]				,					lowercase__						:					Optional[int]	):



			'''simple docstring'''





			print("""\nVertex Distance"""	)
			for i in range(lowercase__	):
						if dist[i] != float("""inf"""	):
									print(lowercase__				,					"""\t"""				,					int(dist[i]	)				,					end="""\t"""	)
						else:
									print(lowercase__				,					"""\t"""				,					"""INF"""				,					end="""\t"""	)
						print()




def    UpperCAmelCase					(  lowercase__						:					Optional[int]				,					lowercase__						:					Any				,					lowercase__						:					Tuple	):



			'''simple docstring'''





			a__					=  float("""inf"""	)
			a__					=  -1
			for i in range(lowercase__	):
						if (not vset[i]) and mdist[i] < min_val:
									a__					=  i
									a__					=  mdist[i]
			return min_ind




def    UpperCAmelCase					(  lowercase__						:					Optional[Any]				,					lowercase__						:					Tuple				,					lowercase__						:					List[Any]	):



			'''simple docstring'''





			a__					=  [float("""inf"""	) for _ in range(lowercase__	)]
			a__					=  [False for _ in range(lowercase__	)]
			a__					=  0.0

			for _ in range(v - 1	):
						a__					=  min_dist(lowercase__				,					lowercase__				,					lowercase__	)
						a__					=  True

						for i in range(lowercase__	):
									if (
									    (not vset[i])
									    and graph[u][i] != float("""inf"""	)
									    and mdist[u] + graph[u][i] < mdist[i]
									):
												a__					=  mdist[u] + graph[u][i]

			print_dist(lowercase__				,					lowercase__	)


if __name__ == "__main__":
	_lowercase				:					Any     					=int(input("""Enter number of vertices: """).strip())
	_lowercase				:					List[str]     					=int(input("""Enter number of edges: """).strip())

	_lowercase				:					List[str]     					=[[float("""inf""") for i in range(V)] for j in range(V)]

	for i in range(V):
		_lowercase				:					Dict     					=0.0

	for i in range(E):
		print("""\nEdge """, i + 1)
		_lowercase				:					Dict     					=int(input("""Enter source:""").strip())
		_lowercase				:					Optional[Any]     					=int(input("""Enter destination:""").strip())
		_lowercase				:					List[Any]     					=float(input("""Enter weight:""").strip())
		_lowercase				:					List[str]     					=weight

	_lowercase				:					List[Any]     					=int(input("""\nEnter shortest path source:""").strip())
	dijkstra(graph, V, gsrc)

