




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_      :							Union[str, Any]    							=					logging.get_logger(__name__)






@dataclass
class        __lowerCAmelCase					:
			snake_case_ :					nn.Module
			snake_case_ :					List[nn.Module]						=	field(default_factory=UpperCAmelCase__					)
			snake_case_ :					list						=	field(default_factory=UpperCAmelCase__					)







			def UpperCamelCase     (			self       :     Tuple       ,       snake_case__       :     Tuple       ,       snake_case__       :     Tensor       ,       snake_case__       :     Tensor ):




									"""simple docstring"""


									_UpperCAmelCase						=	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       :     Union[str, Any]       ,       snake_case__       :     Tensor ):




									"""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       :     Any ):




									"""simple docstring"""


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






@dataclass
class        __lowerCAmelCase					:
			snake_case_ :					nn.Module
			snake_case_ :					nn.Module
			snake_case_ :					int						=	0
			snake_case_ :					List						=	field(default_factory=UpperCAmelCase__					)
			snake_case_ :					List						=	field(default_factory=UpperCAmelCase__					)







			def __call__(			self       :     List[str]       ,       snake_case__       :     Tensor ):




									"""simple docstring"""


									_UpperCAmelCase						=	Tracker(self.dest )(_A ).parametrized
									_UpperCAmelCase						=	Tracker(self.src )(_A ).parametrized

									_UpperCAmelCase						=	list(filter(lambda snake_case__ : type(_A ) not in self.src_skip       ,       _A ) )
									_UpperCAmelCase						=	list(filter(lambda snake_case__ : 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    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


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

																		all_keys.append((from_key, to_key)		)
																		_UpperCAmelCase						=	f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
																		_UpperCAmelCase						=	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						=	from_state_dict.pop(UpperCAmelCase_		)

						our_model.load_state_dict(UpperCAmelCase_		)
						return our_model


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_ = True      ,       ):

						'''simple docstring'''


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

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

						_UpperCAmelCase						=	name
						print(UpperCAmelCase_		)

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

												# we can use the convnext one
												_UpperCAmelCase						=	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=UpperCAmelCase_      ,       )

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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_ = None      ,       snake_case_ = True		):

						'''simple docstring'''


						_UpperCAmelCase						=	'imagenet-1k-id2label.json'
						_UpperCAmelCase						=	1000

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

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

						_UpperCAmelCase						=	partial(UpperCAmelCase_      ,       num_labels=UpperCAmelCase_      ,       idalabel=UpperCAmelCase_      ,       labelaid=UpperCAmelCase_		)

						_UpperCAmelCase						=	{
						    '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						=	{
						    'van-tiny': van_tiny,
						    'van-small': van_small,
						    'van-base': van_base,
						    'van-large': van_large,
						}

						_UpperCAmelCase						=	{
						    '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(
												    UpperCAmelCase_      ,       names_to_config[model_name]      ,       checkpoint=names_to_original_checkpoints[model_name]      ,       from_model=names_to_original_models[model_name]()      ,       save_directory=UpperCAmelCase_      ,       push_to_hub=UpperCAmelCase_      ,       )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    UpperCAmelCase_      ,       UpperCAmelCase_      ,       checkpoint=names_to_original_checkpoints[model_name]      ,       from_model=names_to_original_models[model_name]()      ,       save_directory=UpperCAmelCase_      ,       push_to_hub=UpperCAmelCase_      ,       )


if __name__ == "__main__":
				lowercase_      :							List[str]    							=					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_      :							Any    							=					parser.parse_args()
				lowercase_      :							Path    							=					args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				lowercase_      :							Any    							=					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)





"""simple docstring"""







_a	:	Optional[Any]=					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[Any]    )				->     List[Any]:

	'''simple docstring'''





	return c.isdigit()
def     __UpperCAmelCase							(       UpperCAmelCase_       :							int    )				->     List[Any]:

	'''simple docstring'''





	__snake_case     :				Dict 						=					[]

	# iterate over the string in reverse order
	for c in expression.split()[::-1]:
		# push operand to stack
		if is_operand(UpperCAmelCase_    ):
			stack.append(int(UpperCAmelCase_    )    )

		else:
			# pop values from stack can calculate the result
			# push the result onto the stack again
			__snake_case     :				Optional[Any] 						=					stack.pop()
			__snake_case     :				int 						=					stack.pop()
			stack.append(calc[c](UpperCAmelCase_     ,				UpperCAmelCase_    )    )

	return stack.pop()


# Driver code
if __name__ == "__main__":
	_a	:	Union[str, Any]=					"+ 9 * 2 6"
	print(evaluate(test_expression))

	_a	:	List[Any]=					"/ * 10 2 + 4 1 "
	print(evaluate(test_expression))





