



"""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_   						=   logging.get_logger()
@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					nn.Module
		lowerCAmelCase			:					List[nn.Module]   	=				field(default_factory=A_    )
		lowerCAmelCase			:					list   	=				field(default_factory=A_    )







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  Dict			,_snake_case						:  Tensor			,_snake_case						:  Tensor						)					->		Any:






				"""simple docstring"""



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







		def __call__(	self						:  List[Any]			,_snake_case						:  Tensor						)					->		Dict:






				"""simple docstring"""



				for m in self.module.modules():
						self.handles.append(m.register_forward_hook(self._forward_hook						)						)
				self.module(_snake_case						)
				[x.remove() for x in self.handles]
				return self







		@property
		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		Union[str, Any]:






				"""simple docstring"""



				return list(filter(lambda _snake_case						: len(list(x.state_dict().keys()						)						) > 0			,self.traced						)						)





@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					nn.Module
		lowerCAmelCase			:					nn.Module
		lowerCAmelCase			:					int   	=				1
		lowerCAmelCase			:					List   	=				field(default_factory=A_    )
		lowerCAmelCase			:					List   	=				field(default_factory=A_    )
		lowerCAmelCase			:					bool   	=				True







		def __call__(	self						:  Dict			,_snake_case						:  Tensor						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    Tuple				=			Tracker(self.dest						)(_snake_case						).parametrized
				lowercase__   :    Optional[int]				=			Tracker(self.src						)(_snake_case						).parametrized

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

				if len(_snake_case						) != len(_snake_case						) and self.raise_if_mismatch:
						raise Exception(
						    f"""Numbers of operations are different. Source module has {len(_snake_case						)} operations while"""
						    f""" destination module has {len(_snake_case						)}."""						)

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





class        __A       (	nn.Module    ):







		'''simple docstring'''





		def __init__(	self						:  Any			,_snake_case						:  nn.Module						)					->		List[str]:






				"""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__   :    List[str]				=			len(_snake_case						) + 1
						feature_blocks.append((f"""res{block_index}""", v)						)

				lowercase__   :    List[Any]				=			nn.ModuleDict(_snake_case						)







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  Tensor						)					->		Tuple:






				"""simple docstring"""



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





class        __A       (	A_    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  str						)					->		str:






				"""simple docstring"""



				lowercase__   :    int				=			x.split('''-'''						)
				return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]						)







		def __getitem__(	self						:  Any			,_snake_case						:  str						)					->		Callable[[], Tuple[nn.Module, Dict]]:






				"""simple docstring"""



				if x not in self:
						lowercase__   :    Optional[Any]				=			self.convert_name_to_timm(_snake_case						)
						lowercase__   :    List[str]				=			partial(lambda: (timm.create_model(_snake_case			,pretrained=_snake_case						).eval(), None)						)

				else:
						lowercase__   :    Dict				=			super().__getitem__(_snake_case						)

				return val





class        __A       (	A_    ):







		'''simple docstring'''





		def __getitem__(	self						:  Tuple			,_snake_case						:  str						)					->		Callable[[], nn.Module]:






				"""simple docstring"""



				if "seer" in x and "in1k" not in x:
						lowercase__   :    Any				=			RegNetModel
				else:
						lowercase__   :    Any				=			RegNetForImageClassification
				return val





def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[int]:
		for from_key, to_key in keys:
				lowercase__   :    Union[str, Any]				=			from_state_dict[from_key].clone()
				print(f"""Copied key={from_key} to={to_key}"""	)
		return to_state_dict



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase = True						,     )							->      List[Any]:
		print(f"""Converting {name}..."""	)
		with torch.no_grad():
				lowercase__		,			lowercase__   :    Tuple				=			from_model_func()
				lowercase__   :    Dict				=			our_model_func(__lowerCamelCase	).eval()
				lowercase__   :    str				=			ModuleTransfer(src=__lowerCamelCase						,     dest=__lowerCamelCase						,     raise_if_mismatch=__lowerCamelCase	)
				lowercase__   :    List[str]				=			torch.randn((1, 3, 2_24, 2_24)	)
				module_transfer(__lowerCamelCase	)

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

		lowercase__   :    int				=			our_model(__lowerCamelCase						,     output_hidden_states=__lowerCamelCase	)
		lowercase__   :    Tuple				=			(
		    our_outputs.logits if isinstance(__lowerCamelCase						,     __lowerCamelCase	) else our_outputs.last_hidden_state
		)

		lowercase__   :    Union[str, Any]				=			from_model(__lowerCamelCase	)
		lowercase__   :    Union[str, Any]				=			from_output[-1] if type(__lowerCamelCase	) 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:
				lowercase__   :    List[str]				=			our_outputs.hidden_states[-1]

		assert torch.allclose(__lowerCamelCase						,     __lowerCamelCase	), "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=__lowerCamelCase						,     )

				lowercase__   :    Any				=			2_24 if '''seer''' not in name else 3_84
				# we can use the convnext one
				lowercase__   :    Union[str, Any]				=			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''						,     size=__lowerCamelCase	)
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / name						,     commit_message='''Add image processor'''						,     use_temp_dir=__lowerCamelCase						,     )

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



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = None						,     __lowerCamelCase = True	)							->      Optional[int]:
		lowercase__   :    List[Any]				=			'''imagenet-1k-id2label.json'''
		lowercase__   :    Union[str, Any]				=			10_00
		lowercase__   :    Dict				=			(1, num_labels)

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

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

		lowercase__   :    Dict				=			partial(__lowerCamelCase						,     num_labels=__lowerCamelCase						,     idalabel=__lowerCamelCase						,     labelaid=__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			{
		    '''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	),
		}

		lowercase__   :    str				=			NameToOurModelFuncMap()
		lowercase__   :    Union[str, Any]				=			NameToFromModelFuncMap()
		# add seer weights logic

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

		# pretrained
		lowercase__   :    Dict				=			partial(
		    __lowerCamelCase						,     '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''						,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)						,     )

		lowercase__   :    int				=			partial(
		    __lowerCamelCase						,     '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''						,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)						,     )

		lowercase__   :    str				=			partial(
		    __lowerCamelCase						,     '''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()	)						,     )

		lowercase__   :    Any				=			partial(
		    __lowerCamelCase						,     '''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
		lowercase__   :    Union[str, Any]				=			partial(
		    __lowerCamelCase						,     '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''						,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)						,     )

		lowercase__   :    Optional[Any]				=			partial(
		    __lowerCamelCase						,     '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''						,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)						,     )

		lowercase__   :    int				=			partial(
		    __lowerCamelCase						,     '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''						,     lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	)						,     )

		lowercase__   :    Dict				=			partial(
		    __lowerCamelCase						,     '''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(
				    __lowerCamelCase						,     names_to_from_model_map[model_name]						,     names_to_ours_model_map[model_name]						,     names_to_config[model_name]						,     __lowerCamelCase						,     __lowerCamelCase						,     )
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(
						    __lowerCamelCase						,     names_to_from_model_map[model_name]						,     names_to_ours_model_map[model_name]						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     )
		return config, expected_shape


if __name__ == "__main__":
							lowerCAmelCase_   						=   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_   						=   parser.parse_args()

							lowerCAmelCase_   						=   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      __UpperCAmelCase      (    __lowerCamelCase = 50	)							->      int:
		lowercase__   :    int				=			[[0] * 3 for _ in range(length + 1	)]

		for row_length in range(length + 1	):
				for tile_length in range(2						,     5	):
						for tile_start in range(row_length - tile_length + 1	):
								different_colour_ways_number[row_length][tile_length - 2] += (
								    different_colour_ways_number[row_length - tile_start - tile_length][
								        tile_length - 2
								    ]
								    + 1
								)

		return sum(different_colour_ways_number[length]	)


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