



'''simple docstring'''



import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
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.utils import logging


logging.set_verbosity_info()
lowerCAmelCase	:		Optional[int]				=   logging.get_logger()






@dataclass
class 					UpperCAmelCase__	:
					a      :     List[Any]        =							4_2
					a      :     Optional[int]        =							field(default_factory=UpperCamelCase__ )
					a      :     Union[str, Any]        =							field(default_factory=UpperCamelCase__ )



					def        UpperCAmelCase_						(    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )     ->			str:
						__lowerCAmelCase												=  len(list(m.modules()     )     ) == 1 or isinstance(UpperCAmelCase__			,				nn.Convad     ) or isinstance(UpperCAmelCase__			,				nn.BatchNormad     )
						if has_not_submodules:
							self.traced.append(UpperCAmelCase__     )



					def __call__(    self			,				UpperCamelCase     )     ->			Dict:
						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        UpperCAmelCase_						(    self     )     ->			Any:
						# 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 					UpperCAmelCase__	:
					a      :     str        =							4_2
					a      :     List[str]        =							4_2
					a      :     Tuple        =							1
					a      :     int        =							field(default_factory=UpperCamelCase__ )
					a      :     List[str]        =							field(default_factory=UpperCamelCase__ )
					a      :     Union[str, Any]        =							True



					def __call__(    self			,				UpperCamelCase     )     ->			List[Any]:
						__lowerCAmelCase												=  Tracker(self.dest     )(UpperCAmelCase__     ).parametrized
						__lowerCAmelCase												=  Tracker(self.src     )(UpperCAmelCase__     ).parametrized

						__lowerCAmelCase												=  list(filter(lambda UpperCamelCase     : type(UpperCAmelCase__     ) not in self.src_skip			,				UpperCAmelCase__     )     )
						__lowerCAmelCase												=  list(filter(lambda UpperCamelCase     : type(UpperCAmelCase__     ) not in self.dest_skip			,				UpperCAmelCase__     )     )

						if len(UpperCAmelCase__     ) != len(UpperCAmelCase__     ) and self.raise_if_mismatch:
							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}'''     )






class 					UpperCAmelCase__	(		nn.Module ):



					def __init__(    self			,				UpperCamelCase     )     ->			List[str]:
						super().__init__()

						__lowerCAmelCase												=  []
						# - 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}'''
							__lowerCAmelCase												=  len(UpperCAmelCase__     ) + 1
							feature_blocks.append((F'''res{block_index}''', v)     )

						__lowerCAmelCase												=  nn.ModuleDict(UpperCAmelCase__     )



					def        UpperCAmelCase_						(    self			,				UpperCamelCase     )     ->			Optional[Any]:
						return get_trunk_forward_outputs(
						    UpperCAmelCase__			,				out_feat_keys=UpperCAmelCase__			,				feature_blocks=self._feature_blocks			,				)






class 					UpperCAmelCase__	(		UpperCamelCase__ ):



					def        UpperCAmelCase_						(    self			,				UpperCamelCase     )     ->			str:
						__lowerCAmelCase												=  x.split("-"     )
						return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]     )



					def __getitem__(    self			,				UpperCamelCase     )     ->			Callable[[], Tuple[nn.Module, Dict]]:
						# default to timm!
						if x not in self:
							__lowerCAmelCase												=  self.convert_name_to_timm(UpperCAmelCase__     )
							__lowerCAmelCase												=  partial(lambda: (timm.create_model(UpperCAmelCase__			,				pretrained=UpperCAmelCase__     ).eval(), None)     )

						else:
							__lowerCAmelCase												=  super().__getitem__(UpperCAmelCase__     )

						return val






class 					UpperCAmelCase__	(		UpperCamelCase__ ):



					def __getitem__(    self			,				UpperCamelCase     )     ->			Callable[[], nn.Module]:
						if "seer" in x and "in1k" not in x:
							__lowerCAmelCase												=  RegNetModel
						else:
							__lowerCAmelCase												=  RegNetForImageClassification
						return val



def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						List[Any]				,		lowerCamelCase   :						List[Tuple[str, str]]					):






	'''simple docstring'''




	for from_key, to_key in keys:
		__lowerCAmelCase												=  from_state_dict[from_key].clone()
		print(f'''Copied key={from_key} to={to_key}'''					)
	return to_state_dict



def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						Callable[[], nn.Module]				,		lowerCamelCase   :						Callable[[], nn.Module]				,		lowerCamelCase   :						RegNetConfig				,		lowerCamelCase   :						Path				,		lowerCamelCase   :						bool = True				,		):






	'''simple docstring'''




	print(f'''Converting {name}...'''					)
	with torch.no_grad():
		__lowerCAmelCase							,       __lowerCAmelCase												=  from_model_func()
		__lowerCAmelCase												=  our_model_func(_UpperCamelCase					).eval()
		__lowerCAmelCase												=  ModuleTransfer(src=_UpperCamelCase				,		dest=_UpperCamelCase				,		raise_if_mismatch=_UpperCamelCase					)
		__lowerCAmelCase												=  torch.randn((1, 3, 2_24, 2_24)					)
		module_transfer(_UpperCamelCase					)

	if from_state_dict is not None:
		__lowerCAmelCase												=  []
		# for seer - in1k finetuned we have to manually copy the head
		if "seer" in name and "in1k" in name:
			__lowerCAmelCase												=  [("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
		__lowerCAmelCase												=  manually_copy_vissl_head(_UpperCamelCase				,		our_model.state_dict()				,		_UpperCamelCase					)
		our_model.load_state_dict(_UpperCamelCase					)

	__lowerCAmelCase												=  our_model(_UpperCamelCase				,		output_hidden_states=_UpperCamelCase					)
	__lowerCAmelCase												=  (
	    our_outputs.logits if isinstance(_UpperCamelCase				,		_UpperCamelCase					) else our_outputs.last_hidden_state
	)

	__lowerCAmelCase												=  from_model(_UpperCamelCase					)
	__lowerCAmelCase												=  from_output[-1] if type(_UpperCamelCase					) is list else from_output

	# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
	if "seer" in name and "in1k" in name:
		__lowerCAmelCase												=  our_outputs.hidden_states[-1]

	assert torch.allclose(_UpperCamelCase				,		_UpperCamelCase					), "The model logits don't match the original one."

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

		__lowerCAmelCase												=  2_24 if "seer" not in name else 3_84
		# we can use the convnext one
		__lowerCAmelCase												=  AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"				,		size=_UpperCamelCase					)
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / name				,		commit_message="Add image processor"				,		use_temp_dir=_UpperCamelCase				,		)

		print(f'''Pushed {name}'''					)



def   __lowerCAmelCase					(							lowerCamelCase   :						Path				,		lowerCamelCase   :						str = None				,		lowerCamelCase   :						bool = True					):






	'''simple docstring'''




	__lowerCAmelCase												=  "imagenet-1k-id2label.json"
	__lowerCAmelCase												=  10_00
	__lowerCAmelCase												=  (1, num_labels)

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

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

	__lowerCAmelCase												=  partial(_UpperCamelCase				,		num_labels=_UpperCamelCase				,		idalabel=_UpperCamelCase				,		labelaid=_UpperCamelCase					)

	__lowerCAmelCase												=  {
	    "regnet-x-002": ImageNetPreTrainedConfig(
	        depths=[1, 1, 4, 7]				,		hidden_sizes=[24, 56, 1_52, 3_68]				,		groups_width=8				,		layer_type="x"					),
	    "regnet-x-004": ImageNetPreTrainedConfig(
	        depths=[1, 2, 7, 12]				,		hidden_sizes=[32, 64, 1_60, 3_84]				,		groups_width=16				,		layer_type="x"					),
	    "regnet-x-006": ImageNetPreTrainedConfig(
	        depths=[1, 3, 5, 7]				,		hidden_sizes=[48, 96, 2_40, 5_28]				,		groups_width=24				,		layer_type="x"					),
	    "regnet-x-008": ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 5]				,		hidden_sizes=[64, 1_28, 2_88, 6_72]				,		groups_width=16				,		layer_type="x"					),
	    "regnet-x-016": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 2]				,		hidden_sizes=[72, 1_68, 4_08, 9_12]				,		groups_width=24				,		layer_type="x"					),
	    "regnet-x-032": ImageNetPreTrainedConfig(
	        depths=[2, 6, 15, 2]				,		hidden_sizes=[96, 1_92, 4_32, 10_08]				,		groups_width=48				,		layer_type="x"					),
	    "regnet-x-040": ImageNetPreTrainedConfig(
	        depths=[2, 5, 14, 2]				,		hidden_sizes=[80, 2_40, 5_60, 13_60]				,		groups_width=40				,		layer_type="x"					),
	    "regnet-x-064": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1]				,		hidden_sizes=[1_68, 3_92, 7_84, 16_24]				,		groups_width=56				,		layer_type="x"					),
	    "regnet-x-080": ImageNetPreTrainedConfig(
	        depths=[2, 5, 15, 1]				,		hidden_sizes=[80, 2_40, 7_20, 19_20]				,		groups_width=1_20				,		layer_type="x"					),
	    "regnet-x-120": ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1]				,		hidden_sizes=[2_24, 4_48, 8_96, 22_40]				,		groups_width=1_12				,		layer_type="x"					),
	    "regnet-x-160": ImageNetPreTrainedConfig(
	        depths=[2, 6, 13, 1]				,		hidden_sizes=[2_56, 5_12, 8_96, 20_48]				,		groups_width=1_28				,		layer_type="x"					),
	    "regnet-x-320": ImageNetPreTrainedConfig(
	        depths=[2, 7, 13, 1]				,		hidden_sizes=[3_36, 6_72, 13_44, 25_20]				,		groups_width=1_68				,		layer_type="x"					),
	    # y variant
	    "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]				,		hidden_sizes=[24, 56, 1_52, 3_68]				,		groups_width=8					),
	    "regnet-y-004": ImageNetPreTrainedConfig(
	        depths=[1, 3, 6, 6]				,		hidden_sizes=[48, 1_04, 2_08, 4_40]				,		groups_width=8					),
	    "regnet-y-006": ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 4]				,		hidden_sizes=[48, 1_12, 2_56, 6_08]				,		groups_width=16					),
	    "regnet-y-008": ImageNetPreTrainedConfig(
	        depths=[1, 3, 8, 2]				,		hidden_sizes=[64, 1_28, 3_20, 7_68]				,		groups_width=16					),
	    "regnet-y-016": ImageNetPreTrainedConfig(
	        depths=[2, 6, 17, 2]				,		hidden_sizes=[48, 1_20, 3_36, 8_88]				,		groups_width=24					),
	    "regnet-y-032": ImageNetPreTrainedConfig(
	        depths=[2, 5, 13, 1]				,		hidden_sizes=[72, 2_16, 5_76, 15_12]				,		groups_width=24					),
	    "regnet-y-040": ImageNetPreTrainedConfig(
	        depths=[2, 6, 12, 2]				,		hidden_sizes=[1_28, 1_92, 5_12, 10_88]				,		groups_width=64					),
	    "regnet-y-064": ImageNetPreTrainedConfig(
	        depths=[2, 7, 14, 2]				,		hidden_sizes=[1_44, 2_88, 5_76, 12_96]				,		groups_width=72					),
	    "regnet-y-080": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1]				,		hidden_sizes=[1_68, 4_48, 8_96, 20_16]				,		groups_width=56					),
	    "regnet-y-120": ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1]				,		hidden_sizes=[2_24, 4_48, 8_96, 22_40]				,		groups_width=1_12					),
	    "regnet-y-160": ImageNetPreTrainedConfig(
	        depths=[2, 4, 11, 1]				,		hidden_sizes=[2_24, 4_48, 12_32, 30_24]				,		groups_width=1_12					),
	    "regnet-y-320": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]				,		hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,		groups_width=2_32					),
	    # models created by SEER -> https://arxiv.org/abs/2202.08360
	    "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]				,		hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,		groups_width=2_32					),
	    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]				,		hidden_sizes=[3_28, 9_84, 19_68, 49_20]				,		groups_width=3_28					),
	    "regnet-y-1280-seer": RegNetConfig(
	        depths=[2, 7, 17, 1]				,		hidden_sizes=[5_28, 10_56, 29_04, 73_92]				,		groups_width=2_64					),
	    "regnet-y-2560-seer": RegNetConfig(
	        depths=[3, 7, 16, 1]				,		hidden_sizes=[6_40, 16_96, 25_44, 50_88]				,		groups_width=6_40					),
	    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]				,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,		groups_width=10_10					),
	    # finetuned on imagenet
	    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]				,		hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,		groups_width=2_32					),
	    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]				,		hidden_sizes=[3_28, 9_84, 19_68, 49_20]				,		groups_width=3_28					),
	    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]				,		hidden_sizes=[5_28, 10_56, 29_04, 73_92]				,		groups_width=2_64					),
	    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[3, 7, 16, 1]				,		hidden_sizes=[6_40, 16_96, 25_44, 50_88]				,		groups_width=6_40					),
	    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]				,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,		groups_width=10_10					),
	}

	__lowerCAmelCase												=  NameToOurModelFuncMap()
	__lowerCAmelCase												=  NameToFromModelFuncMap()
	# add seer weights logic

	def load_using_classy_vision(lowerCamelCase   :						str				,		lowerCamelCase   :						Callable[[], nn.Module]					) -> Tuple[nn.Module, Dict]:
		__lowerCAmelCase												=  torch.hub.load_state_dict_from_url(_UpperCamelCase				,		model_dir=str(_UpperCamelCase					)				,		map_location="cpu"					)
		__lowerCAmelCase												=  model_func()
		# check if we have a head, if yes add it
		__lowerCAmelCase												=  files["classy_state_dict"]["base_model"]["model"]
		__lowerCAmelCase												=  model_state_dict["trunk"]
		model.load_state_dict(_UpperCamelCase					)
		return model.eval(), model_state_dict["heads"]

	# pretrained
	__lowerCAmelCase												=  partial(
	    _UpperCamelCase				,		"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"				,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)				,		)

	__lowerCAmelCase												=  partial(
	    _UpperCamelCase				,		"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"				,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)				,		)

	__lowerCAmelCase												=  partial(
	    _UpperCamelCase				,		"https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"				,		lambda: FakeRegNetVisslWrapper(RegNetYaaagf()					)				,		)

	__lowerCAmelCase												=  partial(
	    _UpperCamelCase				,		"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"				,		lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=27				,		group_width=10_10				,		w_a=17_44				,		w_a=6_2_0.8_3				,		w_m=2.5_2					)					)					)				,		)

	# IN1K finetuned
	__lowerCAmelCase												=  partial(
	    _UpperCamelCase				,		"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"				,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)				,		)

	__lowerCAmelCase												=  partial(
	    _UpperCamelCase				,		"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"				,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)				,		)

	__lowerCAmelCase												=  partial(
	    _UpperCamelCase				,		"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"				,		lambda: FakeRegNetVisslWrapper(RegNetYaaagf()					)				,		)

	__lowerCAmelCase												=  partial(
	    _UpperCamelCase				,		"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"				,		lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=27				,		group_width=10_10				,		w_a=17_44				,		w_a=6_2_0.8_3				,		w_m=2.5_2					)					)					)				,		)

	if model_name:
		convert_weight_and_push(
		    _UpperCamelCase				,		names_to_from_model_map[model_name]				,		names_to_ours_model_map[model_name]				,		names_to_config[model_name]				,		_UpperCamelCase				,		_UpperCamelCase				,		)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(
			    _UpperCamelCase				,		names_to_from_model_map[model_name]				,		names_to_ours_model_map[model_name]				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)
	return config, expected_shape


if __name__ == "__main__":
	lowerCAmelCase	:		int				=   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.''',
	)

	lowerCAmelCase	:		Union[str, Any]				=   parser.parse_args()

	lowerCAmelCase	:		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'''



def   __lowerCAmelCase					(							lowerCamelCase   :						Union[str, Any]					):






	'''simple docstring'''




	__lowerCAmelCase												=  1
	__lowerCAmelCase												=  2
	while i * i <= n:
		__lowerCAmelCase												=  0
		while n % i == 0:
			n //= i
			multiplicity += 1
		n_divisors *= multiplicity + 1
		i += 1
	if n > 1:
		n_divisors *= 2
	return n_divisors



def   __lowerCAmelCase					(							):






	'''simple docstring'''




	__lowerCAmelCase												=  1
	__lowerCAmelCase												=  1

	while True:
		i += 1
		t_num += i

		if count_divisors(lowerCamelCase					) > 5_00:
			break

	return t_num


if __name__ == "__main__":
	print(solution())