
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 							lowercase						:
							lowercase_			:			nn.Module
							lowercase_			:			List[nn.Module]			   =field(default_factory=SCREAMING_SNAKE_CASE__						)
							lowercase_			:			list			   =field(default_factory=SCREAMING_SNAKE_CASE__						)




							def 					A__ (							self  ,A__  ,A__  ,A__):
												lowercase		      =		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  ,A__):
												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):
												# 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 							lowercase						:
							lowercase_			:			nn.Module
							lowercase_			:			nn.Module
							lowercase_			:			int			   =0
							lowercase_			:			List			   =field(default_factory=SCREAMING_SNAKE_CASE__						)
							lowercase_			:			List			   =field(default_factory=SCREAMING_SNAKE_CASE__						)




							def __call__(							self  ,A__):
												lowercase		      =		Tracker(self.dest)(A__).parametrized
												lowercase		      =		Tracker(self.src)(A__).parametrized

												lowercase		      =		list(filter(lambda A__: type(A__) not in self.src_skip  ,A__))
												lowercase		      =		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__					,     lowerCAmelCase__						):
					'''simple docstring'''
					# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
					lowercase		      =		from_model.state_dict()
					lowercase		      =		our_model.state_dict()
					lowercase		      =		our_model.config
					lowercase		      =		[]
					for stage_idx in range(len(config.hidden_sizes						)						):
										for block_id in range(config.depths[stage_idx]						):
															lowercase		      =		f'block{stage_idx + 1}.{block_id}.layer_scale_1'
															lowercase		      =		f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

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

					our_model.load_state_dict(lowerCAmelCase__						)
					return our_model



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = True					,     ):
					'''simple docstring'''
					print(f'Downloading weights for {name}...'						)
					lowercase		      =		cached_download(lowerCAmelCase__						)
					print(f'Converting {name}...'						)
					lowercase		      =		torch.load(lowerCAmelCase__						)['''state_dict''']
					from_model.load_state_dict(lowerCAmelCase__						)
					from_model.eval()
					with torch.no_grad():
										lowercase		      =		VanForImageClassification(lowerCAmelCase__						).eval()
										lowercase		      =		ModuleTransfer(src=lowerCAmelCase__					,     dest=lowerCAmelCase__						)
										lowercase		      =		torch.randn((1, 3, 224, 224)						)
										module_transfer(lowerCAmelCase__						)
										lowercase		      =		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.'''						)

					lowercase		      =		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
										lowercase		      =		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__					,     lowerCAmelCase__ = None					,     lowerCAmelCase__ = True						):
					'''simple docstring'''
					lowercase		      =		'''imagenet-1k-id2label.json'''
					lowercase		      =		1000

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

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

					lowercase		      =		partial(lowerCAmelCase__					,     num_labels=lowerCAmelCase__					,     idalabel=lowerCAmelCase__					,     labelaid=lowerCAmelCase__						)

					lowercase		      =		{
					    '''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]					,     ),
					}

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

					lowercase		      =		{
					    '''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__						:Union[str, 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.",
							)

							lowercase__						:Dict					    =       parser.parse_args()
							lowercase__						:Path					    =       args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							lowercase__						: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)






def 			UpperCamelCase       (				lowerCAmelCase__ = 30						):
					'''simple docstring'''
					lowercase		      =		exponent + 2
					lowercase		      =		(1 + 5**0.5) / 2
					lowercase		      =		(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

					return int(lowerCAmelCase__						)


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




