





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()
_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_    :
						__magic_name__:      nn.Module
						__magic_name__:      List[nn.Module]			   =							field(default_factory=snake_case_       )
						__magic_name__:      list			   =							field(default_factory=snake_case_       )






						def UpperCAmelCase_					(   self   :   Optional[int]		, _A   :   Any		, _A   :   Tensor		, _A   :   Tensor						)				->     List[str]:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						len(list(m.modules()						)						) == 1 or isinstance(_A		, nn.Convad						) or isinstance(_A		, nn.BatchNormad						)
							if has_not_submodules:
								if not isinstance(_A		, _A						):
									self.traced.append(_A						)






						def __call__(   self   :   str		, _A   :   Tensor						)				->     Any:







							"""simple docstring"""


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






						@property
						def UpperCAmelCase_					(   self   :   str						)				->     Dict:







							"""simple docstring"""


							return list(filter(lambda _A						: len(list(x.state_dict().keys()						)						) > 0		, self.traced						)						)
@dataclass
class SCREAMING_SNAKE_CASE_    :
						__magic_name__:      nn.Module
						__magic_name__:      nn.Module
						__magic_name__:      int			   =							0
						__magic_name__:      List			   =							field(default_factory=snake_case_       )
						__magic_name__:      List			   =							field(default_factory=snake_case_       )






						def __call__(   self   :   Optional[int]		, _A   :   Tensor						)				->     Optional[Any]:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						Tracker(self.dest						)(_A						).parametrized
							snake_case_		:      List[str]           =						Tracker(self.src						)(_A						).parametrized

							snake_case_		:      List[str]           =						list(filter(lambda _A						: type(_A						) not in self.src_skip		, _A						)						)
							snake_case_		:      Union[str, Any]           =						list(filter(lambda _A						: type(_A						) not in self.dest_skip		, _A						)						)

							if len(_A						) != len(_A						):
								raise Exception(
								    F"""Numbers of operations are different. Source module has {len(_A						)} operations while"""
								    F""" destination module has {len(_A						)}."""						)

							for dest_m, src_m in zip(_A		, _A						):
								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__    (     __a ,			__a ):
	# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
	snake_case_		:      Dict           =						from_model.state_dict()
	snake_case_		:      List[str]           =						our_model.state_dict()
	snake_case_		:      int           =						our_model.config
	snake_case_		:      Dict           =						[]
	for stage_idx in range(len(config.hidden_sizes ) ):
		for block_id in range(config.depths[stage_idx] ):
			snake_case_		:      Optional[Any]           =						f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
			snake_case_		:      Dict           =						f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

			all_keys.append((from_key, to_key) )
			snake_case_		:      str           =						f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
			snake_case_		:      Any           =						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:
		snake_case_		:      Union[str, Any]           =						from_state_dict.pop(__a )

	our_model.load_state_dict(__a )
	return our_model


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ,			__a ,			__a ,			__a = True ,			):
	print(f"""Downloading weights for {name}...""" )
	snake_case_		:      Union[str, Any]           =						cached_download(__a )
	print(f"""Converting {name}...""" )
	snake_case_		:      List[Any]           =						torch.load(__a )['state_dict']
	from_model.load_state_dict(__a )
	from_model.eval()
	with torch.no_grad():
		snake_case_		:      Optional[Any]           =						VanForImageClassification(__a ).eval()
		snake_case_		:      Dict           =						ModuleTransfer(src=__a ,			dest=__a )
		snake_case_		:      Any           =						torch.randn((1, 3, 2_24, 2_24) )
		module_transfer(__a )
		snake_case_		:      Union[str, Any]           =						copy_parameters(__a ,			__a )

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

	snake_case_		:      Tuple           =						name
	print(__a )

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

		# we can use the convnext one
		snake_case_		:      Any           =						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=__a ,			)

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


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a = None ,			__a = True ):
	snake_case_		:      int           =						'imagenet-1k-id2label.json'
	snake_case_		:      Optional[int]           =						10_00

	snake_case_		:      Any           =						'huggingface/label-files'
	snake_case_		:      str           =						num_labels
	snake_case_		:      Tuple           =						json.load(open(hf_hub_download(__a ,			__a ,			repo_type='dataset' ) ,			'r' ) )
	snake_case_		:      Dict           =						{int(__a ): v for k, v in idalabel.items()}

	snake_case_		:      str           =						idalabel
	snake_case_		:      Optional[Any]           =						{v: k for k, v in idalabel.items()}

	snake_case_		:      Union[str, Any]           =						partial(__a ,			num_labels=__a ,			idalabel=__a ,			labelaid=__a )

	snake_case_		:      Tuple           =						{
	    'van-tiny': ImageNetPreTrainedConfig(
	        hidden_sizes=[32, 64, 1_60, 2_56] ,			depths=[3, 3, 5, 2] ,			mlp_ratios=[8, 8, 4, 4] ,			),
	    'van-small': ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 1_28, 3_20, 5_12] ,			depths=[2, 2, 4, 2] ,			mlp_ratios=[8, 8, 4, 4] ,			),
	    'van-base': ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 1_28, 3_20, 5_12] ,			depths=[3, 3, 12, 3] ,			mlp_ratios=[8, 8, 4, 4] ,			),
	    'van-large': ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 1_28, 3_20, 5_12] ,			depths=[3, 5, 27, 3] ,			mlp_ratios=[8, 8, 4, 4] ,			),
	}

	snake_case_		:      List[Any]           =						{
	    'van-tiny': van_tiny,
	    'van-small': van_small,
	    'van-base': van_base,
	    'van-large': van_large,
	}

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


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE   						=		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.""",
						)

						_SCREAMING_SNAKE_CASE   						=		parser.parse_args()
						_SCREAMING_SNAKE_CASE   						=		args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						_SCREAMING_SNAKE_CASE   						=		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__  (							_A      ,      _A			):






  """simple docstring"""


  if not root or root not in binary_tree_mirror_dictionary:
    return
  a_    , a_		    =  binary_tree_mirror_dictionary[root][:2]
  a_		    =  [right_child, left_child]
  binary_tree_mirror_dict(_A      ,      _A			)
  binary_tree_mirror_dict(_A      ,      _A			)






def 				UpperCAmelCase__  (							_A      ,      _A = 1			):






  """simple docstring"""


  if not binary_tree:
    raise ValueError('''binary tree cannot be empty'''			)
  if root not in binary_tree:
    a_		    =  f"root {root} is not present in the binary_tree"
    raise ValueError(_A			)
  a_		    =  dict(_A			)
  binary_tree_mirror_dict(_A      ,      _A			)
  return binary_tree_mirror_dictionary


if __name__ == "__main__":
  UpperCamelCase__       				=	{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F"""Binary tree: {binary_tree}""")
  UpperCamelCase__       				=	binary_tree_mirror(binary_tree, 5)
  print(F"""Binary tree mirror: {binary_tree_mirror_dictionary}""")
