



"""simple docstring"""
import numpy as np



def 				lowerCAmelCase_(      lowercase_   :							List[Any]							)		->				int:
				_lowerCamelCase					     = np.exp(lowercase_							)

				# Add up the all the exponentials
				_lowerCamelCase					     = np.sum(lowercase_							)

				# Divide every exponent by the sum of all exponents
				_lowerCamelCase					     = exponent_vector / sum_of_exponents

				return softmax_vector


if __name__ == "__main__":
			print(softmax((0,)))





"""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()
__SCREAMING_SNAKE_CASE     :  Tuple										=					logging.get_logger(__name__)


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__      :				nn.Module
					lowercase__      :				List[nn.Module]        =       field(default_factory=A__			)
					lowercase__      :				list        =       field(default_factory=A__			)

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = 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 ,				lowerCamelCase__      ):
									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 	snake_case__					(			self      ):
									# 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 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__      :				nn.Module
					lowercase__      :				nn.Module
					lowercase__      :				int        =       0
					lowercase__      :				List        =       field(default_factory=A__			)
					lowercase__      :				List        =       field(default_factory=A__			)

					def __call__(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = Tracker(self.dest      )(lowerCamelCase__      ).parametrized
									_lowerCamelCase					     = Tracker(self.src      )(lowerCamelCase__      ).parametrized

									_lowerCamelCase					     = list(filter(lambda lowerCamelCase__      : type(lowerCamelCase__      ) not in self.src_skip ,				lowerCamelCase__      )      )
									_lowerCamelCase					     = 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 				lowerCAmelCase_(      lowercase_   :							nn.Module  ,      lowercase_   :							nn.Module							)		->				nn.Module:
				# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
				_lowerCamelCase					     = from_model.state_dict()
				_lowerCamelCase					     = our_model.state_dict()
				_lowerCamelCase					     = our_model.config
				_lowerCamelCase					     = []
				for stage_idx in range(len(config.hidden_sizes							)							):
								for block_id in range(config.depths[stage_idx]							):
												_lowerCamelCase					     = F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
												_lowerCamelCase					     = F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

												all_keys.append((from_key, to_key)							)
												_lowerCamelCase					     = F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
												_lowerCamelCase					     = 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:
								_lowerCamelCase					     = from_state_dict.pop(lowercase_							)

				our_model.load_state_dict(lowercase_							)
				return our_model



def 				lowerCAmelCase_(      lowercase_   :							str  ,      lowercase_   :							VanConfig  ,      lowercase_   :							str  ,      lowercase_   :							nn.Module  ,      lowercase_   :							Path  ,      lowercase_   :							bool = True  ,      )		->				Union[str, Any]:
				print(F"""Downloading weights for {name}..."""							)
				_lowerCamelCase					     = cached_download(lowercase_							)
				print(F"""Converting {name}..."""							)
				_lowerCamelCase					     = torch.load(lowercase_							)['''state_dict''']
				from_model.load_state_dict(lowercase_							)
				from_model.eval()
				with torch.no_grad():
								_lowerCamelCase					     = VanForImageClassification(lowercase_							).eval()
								_lowerCamelCase					     = ModuleTransfer(src=lowercase_  ,      dest=lowercase_							)
								_lowerCamelCase					     = torch.randn((1, 3, 2_24, 2_24)							)
								module_transfer(lowercase_							)
								_lowerCamelCase					     = 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.'''							)

				_lowerCamelCase					     = 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
								_lowerCamelCase					     = 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 				lowerCAmelCase_(      lowercase_   :							Path  ,      lowercase_   :							str = None  ,      lowercase_   :							bool = True							)		->				List[str]:
				_lowerCamelCase					     = '''imagenet-1k-id2label.json'''
				_lowerCamelCase					     = 10_00

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

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

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

				_lowerCamelCase					     = {
				    '''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]  ,      ),
				}

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

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

			__SCREAMING_SNAKE_CASE     :  int										=					parser.parse_args()
			__SCREAMING_SNAKE_CASE     :  Path										=					args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			__SCREAMING_SNAKE_CASE     :  List[str]										=					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)
