


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()
a__    :							Optional[Any]            =					logging.get_logger()




@dataclass
class 			UpperCamelCase_     :

	"""simple docstring"""





	snake_case__    :  nn.Module
	snake_case__    :  List[nn.Module]    =      field(default_factory=__SCREAMING_SNAKE_CASE)
	snake_case__    :  list    =      field(default_factory=__SCREAMING_SNAKE_CASE)




	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    Any       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    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      :    Dict      ,      UpperCAmelCase__      :    str       )	->     str:
						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      :    Union[str, Any]       )	->     Union[str, Any]:
						return list(filter(lambda UpperCAmelCase__       : len(list(x.state_dict().keys()       )       ) > 0      ,      self.traced       )       )




@dataclass
class 			UpperCamelCase_     :

	"""simple docstring"""





	snake_case__    :  nn.Module
	snake_case__    :  nn.Module
	snake_case__    :  int    =      1
	snake_case__    :  List    =      field(default_factory=__SCREAMING_SNAKE_CASE)
	snake_case__    :  List    =      field(default_factory=__SCREAMING_SNAKE_CASE)
	snake_case__    :  bool    =      True




	def __call__(			self      :    Union[str, Any]      ,      UpperCAmelCase__      :    Optional[int]       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    Tracker(self.dest       )(__UpperCAmelCase       ).parametrized
						__SCREAMING_SNAKE_CASE						       =    Tracker(self.src       )(__UpperCAmelCase       ).parametrized

						__SCREAMING_SNAKE_CASE						       =    list(filter(lambda UpperCAmelCase__       : type(__UpperCAmelCase       ) not in self.src_skip      ,      __UpperCAmelCase       )       )
						__SCREAMING_SNAKE_CASE						       =    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):

	"""simple docstring"""





	def __init__(			self      :    str      ,      UpperCAmelCase__      :    Union[str, Any]       )	->     Optional[int]:
						super().__init__()

						__SCREAMING_SNAKE_CASE						       =    []
						# - 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}"""
											__SCREAMING_SNAKE_CASE						       =    len(__UpperCAmelCase       ) + 1
											feature_blocks.append((F"""res{block_index}""", v)       )

						__SCREAMING_SNAKE_CASE						       =    nn.ModuleDict(__UpperCAmelCase       )




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




class 			UpperCamelCase_     (			__SCREAMING_SNAKE_CASE):

	"""simple docstring"""





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




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

						else:
											__SCREAMING_SNAKE_CASE						       =    super().__getitem__(__UpperCAmelCase       )

						return val




class 			UpperCamelCase_     (			__SCREAMING_SNAKE_CASE):

	"""simple docstring"""





	def __getitem__(			self      :    List[str]      ,      UpperCAmelCase__      :    Optional[Any]       )	->     Callable[[], nn.Module]:
						if "seer" in x and "in1k" not in x:
											__SCREAMING_SNAKE_CASE						       =    RegNetModel
						else:
											__SCREAMING_SNAKE_CASE						       =    RegNetForImageClassification
						return val




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					for from_key, to_key in keys:
										__SCREAMING_SNAKE_CASE						       =    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  ,						):




					'''simple docstring'''


					print(f"""Converting {name}..."""       )
					with torch.no_grad():
										__SCREAMING_SNAKE_CASE						       =    from_model_func()
										__SCREAMING_SNAKE_CASE						       =    our_model_func(lowerCAmelCase__       ).eval()
										__SCREAMING_SNAKE_CASE						       =    ModuleTransfer(src=lowerCAmelCase__  ,						dest=lowerCAmelCase__  ,						raise_if_mismatch=lowerCAmelCase__       )
										__SCREAMING_SNAKE_CASE						       =    torch.randn((1, 3, 224, 224)       )
										module_transfer(lowerCAmelCase__       )

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

					__SCREAMING_SNAKE_CASE						       =    our_model(lowerCAmelCase__  ,						output_hidden_states=lowerCAmelCase__       )
					__SCREAMING_SNAKE_CASE						       =    (
					    our_outputs.logits if isinstance(lowerCAmelCase__  ,						lowerCAmelCase__       ) else our_outputs.last_hidden_state
					)

					__SCREAMING_SNAKE_CASE						       =    from_model(lowerCAmelCase__       )
					__SCREAMING_SNAKE_CASE						       =    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:
										__SCREAMING_SNAKE_CASE						       =    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__  ,						)

										__SCREAMING_SNAKE_CASE						       =    224 if """seer""" not in name else 384
										# we can use the convnext one
										__SCREAMING_SNAKE_CASE						       =    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       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    """imagenet-1k-id2label.json"""
					__SCREAMING_SNAKE_CASE						       =    1000
					__SCREAMING_SNAKE_CASE						       =    (1, num_labels)

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

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

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

					__SCREAMING_SNAKE_CASE						       =    {
					    """regnet-x-002""": ImageNetPreTrainedConfig(
					        depths=[1, 1, 4, 7]  ,						hidden_sizes=[24, 56, 152, 368]  ,						groups_width=8  ,						layer_type="x"       ),
					    """regnet-x-004""": ImageNetPreTrainedConfig(
					        depths=[1, 2, 7, 12]  ,						hidden_sizes=[32, 64, 160, 384]  ,						groups_width=16  ,						layer_type="x"       ),
					    """regnet-x-006""": ImageNetPreTrainedConfig(
					        depths=[1, 3, 5, 7]  ,						hidden_sizes=[48, 96, 240, 528]  ,						groups_width=24  ,						layer_type="x"       ),
					    """regnet-x-008""": ImageNetPreTrainedConfig(
					        depths=[1, 3, 7, 5]  ,						hidden_sizes=[64, 128, 288, 672]  ,						groups_width=16  ,						layer_type="x"       ),
					    """regnet-x-016""": ImageNetPreTrainedConfig(
					        depths=[2, 4, 10, 2]  ,						hidden_sizes=[72, 168, 408, 912]  ,						groups_width=24  ,						layer_type="x"       ),
					    """regnet-x-032""": ImageNetPreTrainedConfig(
					        depths=[2, 6, 15, 2]  ,						hidden_sizes=[96, 192, 432, 1008]  ,						groups_width=48  ,						layer_type="x"       ),
					    """regnet-x-040""": ImageNetPreTrainedConfig(
					        depths=[2, 5, 14, 2]  ,						hidden_sizes=[80, 240, 560, 1360]  ,						groups_width=40  ,						layer_type="x"       ),
					    """regnet-x-064""": ImageNetPreTrainedConfig(
					        depths=[2, 4, 10, 1]  ,						hidden_sizes=[168, 392, 784, 1624]  ,						groups_width=56  ,						layer_type="x"       ),
					    """regnet-x-080""": ImageNetPreTrainedConfig(
					        depths=[2, 5, 15, 1]  ,						hidden_sizes=[80, 240, 720, 1920]  ,						groups_width=120  ,						layer_type="x"       ),
					    """regnet-x-120""": ImageNetPreTrainedConfig(
					        depths=[2, 5, 11, 1]  ,						hidden_sizes=[224, 448, 896, 2240]  ,						groups_width=112  ,						layer_type="x"       ),
					    """regnet-x-160""": ImageNetPreTrainedConfig(
					        depths=[2, 6, 13, 1]  ,						hidden_sizes=[256, 512, 896, 2048]  ,						groups_width=128  ,						layer_type="x"       ),
					    """regnet-x-320""": ImageNetPreTrainedConfig(
					        depths=[2, 7, 13, 1]  ,						hidden_sizes=[336, 672, 1344, 2520]  ,						groups_width=168  ,						layer_type="x"       ),
					    # y variant
					    """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]  ,						hidden_sizes=[24, 56, 152, 368]  ,						groups_width=8       ),
					    """regnet-y-004""": ImageNetPreTrainedConfig(
					        depths=[1, 3, 6, 6]  ,						hidden_sizes=[48, 104, 208, 440]  ,						groups_width=8       ),
					    """regnet-y-006""": ImageNetPreTrainedConfig(
					        depths=[1, 3, 7, 4]  ,						hidden_sizes=[48, 112, 256, 608]  ,						groups_width=16       ),
					    """regnet-y-008""": ImageNetPreTrainedConfig(
					        depths=[1, 3, 8, 2]  ,						hidden_sizes=[64, 128, 320, 768]  ,						groups_width=16       ),
					    """regnet-y-016""": ImageNetPreTrainedConfig(
					        depths=[2, 6, 17, 2]  ,						hidden_sizes=[48, 120, 336, 888]  ,						groups_width=24       ),
					    """regnet-y-032""": ImageNetPreTrainedConfig(
					        depths=[2, 5, 13, 1]  ,						hidden_sizes=[72, 216, 576, 1512]  ,						groups_width=24       ),
					    """regnet-y-040""": ImageNetPreTrainedConfig(
					        depths=[2, 6, 12, 2]  ,						hidden_sizes=[128, 192, 512, 1088]  ,						groups_width=64       ),
					    """regnet-y-064""": ImageNetPreTrainedConfig(
					        depths=[2, 7, 14, 2]  ,						hidden_sizes=[144, 288, 576, 1296]  ,						groups_width=72       ),
					    """regnet-y-080""": ImageNetPreTrainedConfig(
					        depths=[2, 4, 10, 1]  ,						hidden_sizes=[168, 448, 896, 2016]  ,						groups_width=56       ),
					    """regnet-y-120""": ImageNetPreTrainedConfig(
					        depths=[2, 5, 11, 1]  ,						hidden_sizes=[224, 448, 896, 2240]  ,						groups_width=112       ),
					    """regnet-y-160""": ImageNetPreTrainedConfig(
					        depths=[2, 4, 11, 1]  ,						hidden_sizes=[224, 448, 1232, 3024]  ,						groups_width=112       ),
					    """regnet-y-320""": ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1]  ,						hidden_sizes=[232, 696, 1392, 3712]  ,						groups_width=232       ),
					    # models created by SEER -> https://arxiv.org/abs/2202.08360
					    """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]  ,						hidden_sizes=[232, 696, 1392, 3712]  ,						groups_width=232       ),
					    """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]  ,						hidden_sizes=[328, 984, 1968, 4920]  ,						groups_width=328       ),
					    """regnet-y-1280-seer""": RegNetConfig(
					        depths=[2, 7, 17, 1]  ,						hidden_sizes=[528, 1056, 2904, 7392]  ,						groups_width=264       ),
					    """regnet-y-2560-seer""": RegNetConfig(
					        depths=[3, 7, 16, 1]  ,						hidden_sizes=[640, 1696, 2544, 5088]  ,						groups_width=640       ),
					    """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-320-seer-in1k""": ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1]  ,						hidden_sizes=[232, 696, 1392, 3712]  ,						groups_width=232       ),
					    """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1]  ,						hidden_sizes=[328, 984, 1968, 4920]  ,						groups_width=328       ),
					    """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]  ,						hidden_sizes=[528, 1056, 2904, 7392]  ,						groups_width=264       ),
					    """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
					        depths=[3, 7, 16, 1]  ,						hidden_sizes=[640, 1696, 2544, 5088]  ,						groups_width=640       ),
					    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]  ,						hidden_sizes=[2020, 4040, 1_1110, 2_8280]  ,						groups_width=1010       ),
					}

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

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

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

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

					__SCREAMING_SNAKE_CASE						       =    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()       )  ,						)

					__SCREAMING_SNAKE_CASE						       =    partial(
					    lowerCAmelCase__  ,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"  ,						lambda: FakeRegNetVisslWrapper(
					        RegNet(RegNetParams(depth=27  ,						group_width=1010  ,						w_a=1744  ,						w_a=620.83  ,						w_m=2.52       )       )       )  ,						)

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

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

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

					__SCREAMING_SNAKE_CASE						       =    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=1010  ,						w_a=1744  ,						w_a=620.83  ,						w_m=2.52       )       )       )  ,						)

					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__":
		a__    :							str            =					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__    :							Tuple            =					parser.parse_args()

		a__    :							int            =					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




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					if not nums:
										return 0
					__SCREAMING_SNAKE_CASE						       =    nums[0]
					__SCREAMING_SNAKE_CASE						       =    0
					for num in nums[1:]:
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    (
										    max_excluding + num,
										    max(lowerCAmelCase_  ,						lowerCAmelCase_       ),
										)
					return max(lowerCAmelCase_  ,						lowerCAmelCase_       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
