




'''simple docstring'''







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





@dataclass
class      lowerCamelCase_							:




					'''simple docstring'''






					lowercase__    = 42
					lowercase__    = field(default_factory=UpperCAmelCase_							)
					lowercase__    = field(default_factory=UpperCAmelCase_							)






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ):
							_snake_case	       =       len(list(m.modules() ) ) == 1 or isinstance(__UpperCamelCase,  nn.Convad ) or isinstance(__UpperCamelCase,  nn.BatchNormad )
							if has_not_submodules:
									if not isinstance(__UpperCamelCase,  __UpperCamelCase ):
											self.traced.append(__UpperCamelCase )






					def __call__(    self,  __UpperCamelCase ):
							for m in self.module.modules():
									self.handles.append(m.register_forward_hook(self._forward_hook ) )
							self.module(__UpperCamelCase )
							[x.remove() for x in self.handles]
							return self






					@property
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							# check the len of the state_dict keys to see if we have learnable params
							return list(filter(lambda __UpperCamelCase : len(list(x.state_dict().keys() ) ) > 0,  self.traced ) )





@dataclass
class      lowerCamelCase_							:




					'''simple docstring'''






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






					def __call__(    self,  __UpperCamelCase ):
							_snake_case	       =       Tracker(self.dest )(__UpperCamelCase ).parametrized
							_snake_case	       =       Tracker(self.src )(__UpperCamelCase ).parametrized

							_snake_case	       =       list(filter(lambda __UpperCamelCase : type(__UpperCamelCase ) not in self.src_skip,  __UpperCamelCase ) )
							_snake_case	       =       list(filter(lambda __UpperCamelCase : type(__UpperCamelCase ) not in self.dest_skip,  __UpperCamelCase ) )

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

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






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







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

						all_keys.append((from_key, to_key)  )
						_snake_case	       =       f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
						_snake_case	       =       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	       =       from_state_dict.pop(lowercase_  )

		our_model.load_state_dict(lowercase_  )
		return our_model






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_ = True						,							):



		'''simple docstring'''







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

		_snake_case	       =       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
				_snake_case	       =       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 		_lowercase		(	lowercase_						,							lowercase_ = None						,							lowercase_ = True  ):



		'''simple docstring'''







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

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

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

		_snake_case	       =       partial(lowercase_						,							num_labels=lowercase_						,							idalabel=lowercase_						,							labelaid=lowercase_  )

		_snake_case	       =       {
		    "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]						,							),
		}

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

		_snake_case	       =       {
		    "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__":
						_lowerCamelCase							:						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.',
						)

						_lowerCamelCase							:						Union[str, Any]								=  parser.parse_args()
						_lowerCamelCase							:						Path								=  args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						_lowerCamelCase							:						Optional[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'''







from __future__ import annotations

import math
from itertools import permutations






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5						,							int(math.sqrt(lowercase_  ) + 1  )						,							6  ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






def 		_lowercase		(	lowercase_ = 7  ):



		'''simple docstring'''







		_snake_case	       =       "".join(str(lowercase_  ) for i in range(1						,							n + 1  )  )
		_snake_case	       =       [int("".join(lowercase_  )  ) for i in permutations(lowercase_						,							lowercase_  )]
		_snake_case	       =       [num for num in perm_list if is_prime(lowercase_  )]
		return max(lowercase_  ) if pandigitals else 0


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





