



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()
_snake_case				=					logging.get_logger()






@dataclass
class 		lowerCAmelCase_ :







		"""simple docstring"""
		UpperCAmelCase__													=       42
		UpperCAmelCase__													=       field(default_factory=_a )
		UpperCAmelCase__													=       field(default_factory=_a )
		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE			)     ->  Optional[int]:
									__UpperCamelCase          =  len(list(m.modules()			)			) == 1 or isinstance(_SCREAMING_SNAKE_CASE     ,    nn.Convad			) or isinstance(_SCREAMING_SNAKE_CASE     ,    nn.BatchNormad			)
									if has_not_submodules:
																self.traced.append(_SCREAMING_SNAKE_CASE			)
		def __call__(		self     ,    _SCREAMING_SNAKE_CASE			)     ->  str:

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



		@property
		def 						__lowercase(		self			)     ->  List[str]:
									return list(filter(lambda _SCREAMING_SNAKE_CASE			: len(list(x.state_dict().keys()			)			) > 0     ,    self.traced			)			)






@dataclass
class 		lowerCAmelCase_ :







		"""simple docstring"""
		UpperCAmelCase__													=       42
		UpperCAmelCase__													=       42
		UpperCAmelCase__													=       1
		UpperCAmelCase__													=       field(default_factory=_a )
		UpperCAmelCase__													=       field(default_factory=_a )
		UpperCAmelCase__													=       True
		def __call__(		self     ,    _SCREAMING_SNAKE_CASE			)     ->  Optional[int]:
									__UpperCamelCase          =  Tracker(self.dest			)(_SCREAMING_SNAKE_CASE			).parametrized
									__UpperCamelCase          =  Tracker(self.src			)(_SCREAMING_SNAKE_CASE			).parametrized

									__UpperCamelCase          =  list(filter(lambda _SCREAMING_SNAKE_CASE			: type(_SCREAMING_SNAKE_CASE			) not in self.src_skip     ,    _SCREAMING_SNAKE_CASE			)			)
									__UpperCamelCase          =  list(filter(lambda _SCREAMING_SNAKE_CASE			: type(_SCREAMING_SNAKE_CASE			) not in self.dest_skip     ,    _SCREAMING_SNAKE_CASE			)			)

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

									for dest_m, src_m in zip(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_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 		lowerCAmelCase_ (			nn.Module ):







		"""simple docstring"""
		def __init__(		self     ,    _SCREAMING_SNAKE_CASE			)     ->  Dict:
									super().__init__()

									__UpperCamelCase          =  []
									# - 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}"""
																__UpperCamelCase          =  len(_SCREAMING_SNAKE_CASE			) + 1
																feature_blocks.append((f"""res{block_index}""", v)			)

									__UpperCamelCase          =  nn.ModuleDict(_SCREAMING_SNAKE_CASE			)



		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE			)     ->  Tuple:
									return get_trunk_forward_outputs(
									    _SCREAMING_SNAKE_CASE     ,    out_feat_keys=_SCREAMING_SNAKE_CASE     ,    feature_blocks=self._feature_blocks     ,    )






class 		lowerCAmelCase_ (			_a ):







		"""simple docstring"""
		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE			)     ->  str:
									__UpperCamelCase          =  x.split('-'			)
									return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]			)



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

									if x not in self:
																__UpperCamelCase          =  self.convert_name_to_timm(_SCREAMING_SNAKE_CASE			)
																__UpperCamelCase          =  partial(lambda: (timm.create_model(_SCREAMING_SNAKE_CASE     ,    pretrained=_SCREAMING_SNAKE_CASE			).eval(), None)			)

									else:
																__UpperCamelCase          =  super().__getitem__(_SCREAMING_SNAKE_CASE			)

									return val






class 		lowerCAmelCase_ (			_a ):







		"""simple docstring"""
		def __getitem__(		self     ,    _SCREAMING_SNAKE_CASE			)     ->  Callable[[], nn.Module]:

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


def     _a							(     __lowercase				, __lowercase				, __lowercase		)     ->						List[Any]:

							"""simple docstring"""





							for from_key, to_key in keys:
														__UpperCamelCase          =  from_state_dict[from_key].clone()
														print(F"""Copied key={from_key} to={to_key}"""		)
							return to_state_dict
def     _a							(     __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase				, __lowercase = True				, )     ->						Optional[Any]:

							"""simple docstring"""





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

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

							__UpperCamelCase          =  our_model(_lowerCAmelCase				, output_hidden_states=_lowerCAmelCase		)
							__UpperCamelCase          =  (
							    our_outputs.logits if isinstance(_lowerCAmelCase				, _lowerCAmelCase		) else our_outputs.last_hidden_state
							)

							__UpperCamelCase          =  from_model(_lowerCAmelCase		)
							__UpperCamelCase          =  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:
														__UpperCamelCase          =  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				, )

														__UpperCamelCase          =  224 if 'seer' not in name else 384
														# we can use the convnext one
														__UpperCamelCase          =  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     _a							(     __lowercase				, __lowercase = None				, __lowercase = True		)     ->						str:

							"""simple docstring"""





							__UpperCamelCase          =  'imagenet-1k-id2label.json'
							__UpperCamelCase          =  1000
							__UpperCamelCase          =  (1, num_labels)

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

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

							__UpperCamelCase          =  partial(_lowerCAmelCase				, num_labels=_lowerCAmelCase				, idalabel=_lowerCAmelCase				, labelaid=_lowerCAmelCase		)

							__UpperCamelCase          =  {
							    '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		),
							}

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

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

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

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

							__UpperCamelCase          =  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()		)				, )

							__UpperCamelCase          =  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
							__UpperCamelCase          =  partial(
							    _lowerCAmelCase				, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'				, lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)				, )

							__UpperCamelCase          =  partial(
							    _lowerCAmelCase				, 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'				, lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)				, )

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

							__UpperCamelCase          =  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__":
			_snake_case				=					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.',
			)

			_snake_case				=					parser.parse_args()

			_snake_case				=					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)







def 						_lowerCAmelCase							(     _lowerCAmelCase = 100			)    ->						int:


						'''simple docstring'''






						__snake_case				=		n * (n + 1) * (2 * n + 1) / 6
						__snake_case				=		(n * (n + 1) / 2) ** 2
						return int(square_of_sum - sum_of_squares			)


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





