






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()
UpperCAmelCase_					:		List[str]           = logging.get_logger(__name__)

@dataclass
class        _SCREAMING_SNAKE_CASE			:
	snake_case__				:	nn.Module
	snake_case__				:	List[nn.Module]	      =    field(default_factory=_a )
	snake_case__				:	list	      =    field(default_factory=_a )





	def 		_A					(						self							:   Union[str, Any]					,    __lowerCamelCase							:   List[Any]					,    __lowerCamelCase							:   Tensor					,    __lowerCamelCase							:   Tensor      ):
		UpperCamelCase	:List[Any]		=      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							:   str					,    __lowerCamelCase							:   Tensor      ):
		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							:   Optional[int]      ):
		# check the len of the state_dict keys to see if we have learnable params
		return list(filter(lambda __lowerCamelCase      : len(list(x.state_dict().keys()      )      ) > 0					,    self.traced      )      )

@dataclass
class        _SCREAMING_SNAKE_CASE			:
	snake_case__				:	nn.Module
	snake_case__				:	nn.Module
	snake_case__				:	int	      =    0
	snake_case__				:	List	      =    field(default_factory=_a )
	snake_case__				:	List	      =    field(default_factory=_a )





	def __call__(						self							:   Optional[Any]					,    __lowerCamelCase							:   Tensor      ):
		UpperCamelCase	:Tuple		=      Tracker(self.dest      )(__lowerCamelCase      ).parametrized
		UpperCamelCase	:List[str]		=      Tracker(self.src      )(__lowerCamelCase      ).parametrized

		UpperCamelCase	:str		=      list(filter(lambda __lowerCamelCase      : type(__lowerCamelCase      ) not in self.src_skip					,    __lowerCamelCase      )      )
		UpperCamelCase	:Optional[Any]		=      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   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    nn.Module      ,     __magic_name__   :    nn.Module   )						->     nn.Module:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      from_model.state_dict()
	UpperCamelCase	:Tuple		=      our_model.state_dict()
	UpperCamelCase	:List[str]		=      our_model.config
	UpperCamelCase	:Optional[Any]		=      []
	for stage_idx in range(len(config.hidden_sizes   )   ):
		for block_id in range(config.depths[stage_idx]   ):
			UpperCamelCase	:int		=      f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
			UpperCamelCase	:Tuple		=      f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

			all_keys.append((from_key, to_key)   )
			UpperCamelCase	:int		=      f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
			UpperCamelCase	:str		=      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:
		UpperCamelCase	:str		=      from_state_dict.pop(__magic_name__   )

	our_model.load_state_dict(__magic_name__   )
	return our_model

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    str      ,     __magic_name__   :    VanConfig      ,     __magic_name__   :    str      ,     __magic_name__   :    nn.Module      ,     __magic_name__   :    Path      ,     __magic_name__   :    bool = True      ,     )						->     List[Any]:






	"""simple docstring"""



	print(f"""Downloading weights for {name}..."""   )
	UpperCamelCase	:Optional[int]		=      cached_download(__magic_name__   )
	print(f"""Converting {name}..."""   )
	UpperCamelCase	:Dict		=      torch.load(__magic_name__   )["""state_dict"""]
	from_model.load_state_dict(__magic_name__   )
	from_model.eval()
	with torch.no_grad():
		UpperCamelCase	:Optional[Any]		=      VanForImageClassification(__magic_name__   ).eval()
		UpperCamelCase	:Tuple		=      ModuleTransfer(src=__magic_name__      ,     dest=__magic_name__   )
		UpperCamelCase	:Union[str, Any]		=      torch.randn((1, 3, 224, 224)   )
		module_transfer(__magic_name__   )
		UpperCamelCase	:Any		=      copy_parameters(__magic_name__      ,     __magic_name__   )

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

	UpperCamelCase	:Any		=      name
	print(__magic_name__   )

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

		# we can use the convnext one
		UpperCamelCase	:Dict		=      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=__magic_name__      ,     )

		print(f"""Pushed {checkpoint_name}"""   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Path      ,     __magic_name__   :    str = None      ,     __magic_name__   :    bool = True   )						->     Any:






	"""simple docstring"""



	UpperCamelCase	:Tuple		=      """imagenet-1k-id2label.json"""
	UpperCamelCase	:Tuple		=      1000

	UpperCamelCase	:Any		=      """huggingface/label-files"""
	UpperCamelCase	:Union[str, Any]		=      num_labels
	UpperCamelCase	:List[Any]		=      json.load(open(hf_hub_download(__magic_name__      ,     __magic_name__      ,     repo_type="""dataset"""   )      ,     """r"""   )   )
	UpperCamelCase	:Any		=      {int(__magic_name__   ): v for k, v in idalabel.items()}

	UpperCamelCase	:Union[str, Any]		=      idalabel
	UpperCamelCase	:int		=      {v: k for k, v in idalabel.items()}

	UpperCamelCase	:Dict		=      partial(__magic_name__      ,     num_labels=__magic_name__      ,     idalabel=__magic_name__      ,     labelaid=__magic_name__   )

	UpperCamelCase	:Union[str, Any]		=      {
	    """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]      ,     ),
	}

	UpperCamelCase	:Optional[int]		=      {
	    """van-tiny""": van_tiny,
	    """van-small""": van_small,
	    """van-base""": van_base,
	    """van-large""": van_large,
	}

	UpperCamelCase	:Union[str, Any]		=      {
	    """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(
		    __magic_name__      ,     names_to_config[model_name]      ,     checkpoint=names_to_original_checkpoints[model_name]      ,     from_model=names_to_original_models[model_name]()      ,     save_directory=__magic_name__      ,     push_to_hub=__magic_name__      ,     )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(
			    __magic_name__      ,     __magic_name__      ,     checkpoint=names_to_original_checkpoints[model_name]      ,     from_model=names_to_original_models[model_name]()      ,     save_directory=__magic_name__      ,     push_to_hub=__magic_name__      ,     )


if __name__ == "__main__":
	UpperCAmelCase_					:		Union[str, 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.''',
	)

	UpperCAmelCase_					:		Optional[Any]           = parser.parse_args()
	UpperCAmelCase_					:		Path           = args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	UpperCAmelCase_					:		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)








class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Tuple      ):
		UpperCamelCase	:List[str]		=      [0]
		UpperCamelCase	:Optional[int]		=      0





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:List[str]		=      self.__heap[i]
		while i // 2 > 0:
			if self.__heap[i] > self.__heap[i // 2]:
				UpperCamelCase	:Any		=      self.__heap[i // 2]
				UpperCamelCase	:Optional[Any]		=      temporary
			i //= 2





	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   int      ):
		self.__heap.append(__lowerCamelCase      )
		self.__size += 1
		self.__swap_up(self.__size      )





	def 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   int      ):
		while self.__size >= 2 * i:
			if 2 * i + 1 > self.__size:
				UpperCamelCase	:List[Any]		=      2 * i
			else:
				if self.__heap[2 * i] > self.__heap[2 * i + 1]:
					UpperCamelCase	:Union[str, Any]		=      2 * i
				else:
					UpperCamelCase	:Dict		=      2 * i + 1
			UpperCamelCase	:Optional[Any]		=      self.__heap[i]
			if self.__heap[i] < self.__heap[bigger_child]:
				UpperCamelCase	:List[str]		=      self.__heap[bigger_child]
				UpperCamelCase	:List[str]		=      temporary
			UpperCamelCase	:Optional[int]		=      bigger_child





	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:str		=      self.__heap[1]
		UpperCamelCase	:Tuple		=      self.__heap[self.__size]
		self.__size -= 1
		self.__heap.pop()
		self.__swap_down(1      )
		return max_value





	@property
	def 		_A					(						self							:   int      ):
		return self.__heap[1:]





	def __len__(						self							:   str      ):
		return self.__size


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# create an instance of BinaryHeap
	UpperCAmelCase_					:		Optional[int]           = BinaryHeap()
	binary_heap.insert(6)
	binary_heap.insert(10)
	binary_heap.insert(15)
	binary_heap.insert(12)
	# pop root(max-values because it is max heap)
	print(binary_heap.pop())  # 15
	print(binary_heap.pop())  # 12
	# get the list and size after operations
	print(binary_heap.get_list)
	print(len(binary_heap))
