



'''simple docstring'''

import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
_A					:     str      =logging.get_logger()



@dataclass
class 		lowerCamelCase__   :




					'''simple docstring'''



					A_         =    42
					A_         =    field(default_factory=A  )
					A_         =    field(default_factory=A  )
					A_         =    field(default_factory=A  )




					def     __UpperCAmelCase  (	self					:						Any  ,	UpperCamelCase_					:						Any  ,	UpperCamelCase_					:						Tensor  ,	UpperCamelCase_					:						Tensor  ,	UpperCamelCase_					:						str       )	->							int:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		len(list(m.modules()       )       ) == 1 or isinstance(UpperCamelCase_  ,	nn.Convad       ) or isinstance(UpperCamelCase_  ,	nn.BatchNormad       )
						if has_not_submodules:
							self.traced.append(UpperCamelCase_       )
							_lowercase       :							Optional[Any]  	=		m




					def __call__(	self					:						List[str]  ,	UpperCamelCase_					:						Tensor       )	->							Any:



						'''simple docstring'''





						for name, m in self.module.named_modules():
							self.handles.append(m.register_forward_hook(partial(self._forward_hook  ,	name=UpperCamelCase_       )       )       )
						self.module(UpperCamelCase_       )
						[x.remove() for x in self.handles]
						return self




					@property
					def     __UpperCAmelCase  (	self					:						List[str]       )	->							List[str]:



						'''simple docstring'''





						return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()       )       ) > 0}






class 		lowerCamelCase__   (    nn.Module  ):




					'''simple docstring'''



					def __init__(	self					:						Optional[int]  ,	UpperCamelCase_					:						nn.Module       )	->							Optional[int]:



						'''simple docstring'''





						super().__init__()

						_lowercase       :							List[Tuple[str, nn.Module]]  	=		[]
						# - get the stem
						feature_blocks.append(('conv1', model.stem)       )
						# - get all the feature blocks
						for k, v in model.trunk_output.named_children():
							assert k.startswith('block'       ), F'''Unexpected layer name {k}'''
							_lowercase       :							int  	=		len(UpperCamelCase_       ) + 1
							feature_blocks.append((F'''res{block_index}''', v)       )

						_lowercase       :							Optional[int]  	=		nn.ModuleDict(UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						Tensor       )	->							Tuple:



						'''simple docstring'''





						return get_trunk_forward_outputs(
						    UpperCamelCase_  ,	out_feat_keys=UpperCamelCase_  ,	feature_blocks=self._feature_blocks  ,	)






class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					def     __UpperCAmelCase  (	self					:						List[str]       )	->							List[Any]:



						'''simple docstring'''





						return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]







def     __UpperCamelCase					(				_lowercase       )			->      Dict[str, str]:
	_lowercase       :							Optional[int]  	=		RegNetConfig(depths=[2, 7, 17, 1],					hidden_sizes=[8, 8, 8, 8],					groups_width=8       )
	if "in1k" in model_name:
		_lowercase       :							Dict  	=		RegNetForImageClassification(_lowercase       )
	else:
		_lowercase       :							Tuple  	=		RegNetModel(_lowercase       )
	# create from model (with small weights)
	_lowercase       :							List[str]  	=		FakeRegNetVisslWrapper(
	    RegNet(FakeRegNetParams(depth=27,					group_width=1010,					w_a=1744,					w_a=6_2_0.8_3,					w_m=2.5_2       )       )       )

	with torch.no_grad():
		_lowercase       :							List[Any]  	=		from_model.eval()
		_lowercase       :							List[str]  	=		our_model.eval()

		_lowercase       :							int  	=		torch.randn((1, 3, 32, 32)       )
		# trace both
		_lowercase       :							Dict  	=		Tracker(_lowercase       )
		_lowercase       :							Any  	=		dest_tracker(_lowercase       ).parametrized

		pprint(dest_tracker.nameamodule       )
		_lowercase       :							Optional[int]  	=		Tracker(_lowercase       )
		_lowercase       :							str  	=		src_tracker(_lowercase       ).parametrized

	# convert the keys -> module dict to keys -> params
	def to_params_dict(_lowercase       ):
		_lowercase       :							Optional[int]  	=		OrderedDict()
		for name, module in dict_with_modules.items():
			for param_name, param in module.state_dict().items():
				_lowercase       :							List[str]  	=		param
		return params_dict

	_lowercase       :							int  	=		{}

	_lowercase       :							List[Any]  	=		to_params_dict(_lowercase       )
	_lowercase       :							Optional[Any]  	=		to_params_dict(_lowercase       )

	for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(),					dst_state_dict.items()       ):
		_lowercase       :							int  	=		dest_key
		logger.info(f'''{src_key} -> {dest_key}'''       )
	# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
	if "in1k" in model_name:
		_lowercase       :							Optional[int]  	=		'classifier.1.weight'
		_lowercase       :							Optional[Any]  	=		'classifier.1.bias'

	return from_to_ours_keys







def     __UpperCamelCase					(				_lowercase,					_lowercase = None,					_lowercase = True       )			->      Dict:
	_lowercase       :							Tuple  	=		'imagenet-1k-id2label.json'
	_lowercase       :							Optional[Any]  	=		1000

	_lowercase       :							Tuple  	=		'huggingface/label-files'
	_lowercase       :							Any  	=		num_labels
	_lowercase       :							int  	=		json.load(open(cached_download(hf_hub_url(_lowercase,					_lowercase,					repo_type='dataset'       )       ),					'r'       )       )
	_lowercase       :							List[str]  	=		{int(_lowercase       ): v for k, v in idalabel.items()}

	_lowercase       :							str  	=		idalabel
	_lowercase       :							Dict  	=		{v: k for k, v in idalabel.items()}

	_lowercase       :							str  	=		partial(_lowercase,					num_labels=_lowercase,					idalabel=_lowercase,					labelaid=_lowercase       )

	_lowercase       :							int  	=		{
	    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1],					hidden_sizes=[2020, 4040, 1_1110, 2_8280],					groups_width=1010       ),
	    # finetuned on imagenet
	    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1],					hidden_sizes=[2020, 4040, 1_1110, 2_8280],					groups_width=1010       ),
	}

	# add seer weights logic
	def load_using_classy_vision(_lowercase       ) -> Tuple[Dict, Dict]:
		_lowercase       :							Dict  	=		torch.hub.load_state_dict_from_url(_lowercase,					model_dir=str(_lowercase       ),					map_location='cpu'       )
		# check if we have a head, if yes add it
		_lowercase       :							Union[str, Any]  	=		files['classy_state_dict']['base_model']['model']
		return model_state_dict["trunk"], model_state_dict["heads"]

	_lowercase       :							int  	=		{
	    'regnet-y-10b-seer': partial(
	        _lowercase,					'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch',					),
	    'regnet-y-10b-seer-in1k': partial(
	        _lowercase,					'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch',					),
	}

	_lowercase       :							str  	=		get_from_to_our_keys(_lowercase       )

	if not (save_directory / f'''{model_name}.pth''').exists():
		logger.info('Loading original state_dict.'       )
		_lowercase	,     _lowercase       :							Any  	=		names_to_from_model[model_name]()
		_lowercase       :							Union[str, Any]  	=		from_state_dict_trunk
		if "in1k" in model_name:
			# add the head
			_lowercase       :							Tuple  	=		{**from_state_dict_trunk, **from_state_dict_head}
		logger.info('Done!'       )

		_lowercase       :							Optional[Any]  	=		{}

		_lowercase       :							Dict  	=		list(from_state_dict.keys()       )
		_lowercase       :							Tuple  	=		r'\.block.-part.'
		# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
		for key in from_state_dict.keys():
			# remove the weird "block[0,1]-part" from the key
			_lowercase       :							Union[str, Any]  	=		re.sub(_lowercase,					'',					_lowercase       )
			# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
			_lowercase       :							str  	=		from_to_ours_keys[src_key]
			# store the parameter with our key
			_lowercase       :							Union[str, Any]  	=		from_state_dict[key]
			not_used_keys.remove(_lowercase       )
		# check that all keys have been updated
		assert len(_lowercase       ) == 0, f'''Some keys where not used {",".join(_lowercase       )}'''

		logger.info(f'''The following keys were not used: {",".join(_lowercase       )}'''       )

		# save our state dict to disk
		torch.save(_lowercase,					save_directory / f'''{model_name}.pth'''       )

		del converted_state_dict
	else:
		logger.info('The state_dict was already stored on disk.'       )
	if push_to_hub:
		logger.info(f'''Token is {os.environ["HF_TOKEN"]}'''       )
		logger.info('Loading our model.'       )
		# create our model
		_lowercase       :							str  	=		names_to_config[model_name]
		_lowercase       :							Union[str, Any]  	=		RegNetModel
		if "in1k" in model_name:
			_lowercase       :							Tuple  	=		RegNetForImageClassification
		_lowercase       :							Any  	=		our_model_func(_lowercase       )
		# place our model to the meta device (so remove all the weights)
		our_model.to(torch.device('meta'       )       )
		logger.info('Loading state_dict in our model.'       )
		# load state dict
		_lowercase       :							str  	=		our_model.state_dict().keys()
		PreTrainedModel._load_pretrained_model_low_mem(
		    _lowercase,					_lowercase,					[save_directory / f'''{model_name}.pth''']       )
		logger.info('Finally, pushing!'       )
		# push it to hub
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / model_name,					commit_message='Add model',					output_dir=save_directory / model_name,					)
		_lowercase       :							Optional[int]  	=		384
		# we can use the convnext one
		_lowercase       :							Dict  	=		AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k',					size=_lowercase       )
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / model_name,					commit_message='Add image processor',					output_dir=save_directory / model_name,					)


if __name__ == "__main__":
		_A					:     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 regnet* architecture,'''
		        ''' currently: regnetx-*, regnety-*. 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(
		    '''--push_to_hub''',
		    default=True,
		    type=bool,
		    required=False,
		    help='''If True, push model and image processor to the hub.''',
		)

		_A					:     List[Any]      =parser.parse_args()

		_A					:     Path      =args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





'''simple docstring'''

from __future__ import annotations

import sys







def     __UpperCamelCase					(				_lowercase       )			->      int:
	if n < 0:
		raise ValueError('Negative arguments are not supported'       )
	return _fib(_lowercase       )[0]







def     __UpperCamelCase					(				_lowercase       )			->      tuple[int, int]:
	if n == 0:  # (F(0), F(1))
		return (0, 1)

	# F(2n) = F(n)[2F(n+1) − F(n)]
	# F(2n+1) = F(n+1)^2+F(n)^2
	_lowercase	,     _lowercase       :							List[str]  	=		_fib(n // 2       )
	_lowercase       :							Dict  	=		a * (b * 2 - a)
	_lowercase       :							Union[str, Any]  	=		a * a + b * b
	return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		_A					:     Optional[Any]      =int(sys.argv[1])
		print(F'''fibonacci({n}) is {fibonacci(n)}''')
