





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							:Dict							=	logging.get_logger(__name__)



@dataclass
class  _a  :






							"""simple docstring"""

							snake_case_							=    42
							snake_case_							=    field(default_factory=lowercase__				)
							snake_case_							=    field(default_factory=lowercase__				)

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







@dataclass
class  _a  :






							"""simple docstring"""

							snake_case_							=    42
							snake_case_							=    42
							snake_case_							=    0
							snake_case_							=    field(default_factory=lowercase__				)
							snake_case_							=    field(default_factory=lowercase__				)

							def __call__(   self					:  Union[str, Any]				,						a					:  Tensor     )						->str:
													SCREAMING_SNAKE_CASE__  :					List[str] =       Tracker(self.dest     )(a     ).parametrized
													SCREAMING_SNAKE_CASE__  :					int =       Tracker(self.src     )(a     ).parametrized

													SCREAMING_SNAKE_CASE__  :					Dict =       list(filter(lambda a     : type(a     ) not in self.src_skip				,						a     )     )
													SCREAMING_SNAKE_CASE__  :					int =       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    UpperCAmelCase				(  _lowerCamelCase   :	nn.Module      , _lowerCamelCase   :	nn.Module  ):





						'''simple docstring'''


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

																		all_keys.append((from_key, to_key)  )
																		SCREAMING_SNAKE_CASE__  :					int =       f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
																		SCREAMING_SNAKE_CASE__  :					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:
												SCREAMING_SNAKE_CASE__  :					Any =       from_state_dict.pop(_lowerCamelCase  )

						our_model.load_state_dict(_lowerCamelCase  )
						return our_model







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





						'''simple docstring'''


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

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

						SCREAMING_SNAKE_CASE__  :					int =       name
						print(_lowerCamelCase  )

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

												# we can use the convnext one
												SCREAMING_SNAKE_CASE__  :					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=_lowerCamelCase      , )

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







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





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					int =       "imagenet-1k-id2label.json"
						SCREAMING_SNAKE_CASE__  :					int =       1_000

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

						SCREAMING_SNAKE_CASE__  :					int =       idalabel
						SCREAMING_SNAKE_CASE__  :					Any =       {v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       partial(_lowerCamelCase      , num_labels=_lowerCamelCase      , idalabel=_lowerCamelCase      , labelaid=_lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Tuple =       {
						    "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]      , ),
						}

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

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       {
						    "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(
												    _lowerCamelCase      , names_to_config[model_name]      , checkpoint=names_to_original_checkpoints[model_name]      , from_model=names_to_original_models[model_name]()      , save_directory=_lowerCamelCase      , push_to_hub=_lowerCamelCase      , )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    _lowerCamelCase      , _lowerCamelCase      , checkpoint=names_to_original_checkpoints[model_name]      , from_model=names_to_original_models[model_name]()      , save_directory=_lowerCamelCase      , push_to_hub=_lowerCamelCase      , )


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							:List[Any]							=	parser.parse_args()
				__lowercase							:Path							=	args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				__lowercase							:Tuple							=	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)






from itertools import combinations_with_replacement







def    UpperCAmelCase				(  _lowerCamelCase   :	int = 100  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					list[int] =       [*list(range(1      , 21  )  ), 25]
						SCREAMING_SNAKE_CASE__  :					list[int] =       [2 * x for x in range(1      , 21  )] + [50]
						SCREAMING_SNAKE_CASE__  :					list[int] =       [3 * x for x in range(1      , 21  )]
						SCREAMING_SNAKE_CASE__  :					list[int] =       singles + doubles + triples + [0]

						SCREAMING_SNAKE_CASE__  :					int =       0
						SCREAMING_SNAKE_CASE__  :					int
						SCREAMING_SNAKE_CASE__  :					int
						SCREAMING_SNAKE_CASE__  :					int
						SCREAMING_SNAKE_CASE__  :					int

						for double in doubles:
												for throwa, throwa in combinations_with_replacement(_lowerCamelCase      , 2  ):
																		SCREAMING_SNAKE_CASE__  :					int =       double + throwa + throwa
																		if checkout_total < limit:
																								num_checkouts += 1

						return num_checkouts


if __name__ == "__main__":
				print(f"{solution() = }")