






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()
__lowerCamelCase						= logging.get_logger()

@dataclass
class      UpperCamelCase_							:
						lowercase									=  42
						lowercase									=  field(default_factory=UpperCamelCase       )
						lowercase									=  field(default_factory=UpperCamelCase       )
						lowercase									=  field(default_factory=UpperCamelCase       )




						def      snake_case__(    self		,	lowercase		,	lowercase		,	lowercase		,	lowercase       )			->						Any:
													_a  :    List[Any]			=						len(list(m.modules()       )       ) == 1 or isinstance(lowercase		,	nn.Convad       ) or isinstance(lowercase		,	nn.BatchNormad       )
													if has_not_submodules:
																				self.traced.append(lowercase       )
																				_a  :    Optional[Any]			=						m




						def __call__(    self		,	lowercase       )			->						Optional[int]:
													for name, m in self.module.named_modules():
																				self.handles.append(m.register_forward_hook(partial(self._forward_hook		,	name=lowercase       )       )       )
													self.module(lowercase       )
													[x.remove() for x in self.handles]
													return self







						@property
						def      snake_case__(    self       )			->						Optional[int]:
													# check the len of the state_dict keys to see if we have learnable params
													return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()       )       ) > 0}

class      UpperCamelCase_							(				nn.Module       ):




						def __init__(    self		,	lowercase       )			->						int:
													super().__init__()

													_a  :    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}'
																				_a  :    List[str]			=						len(lowercase       ) + 1
																				feature_blocks.append((F'res{block_index}', v)       )

													_a  :    str			=						nn.ModuleDict(lowercase       )







						def      snake_case__(    self		,	lowercase       )			->						str:
													return get_trunk_forward_outputs(
													    lowercase		,	out_feat_keys=lowercase		,	feature_blocks=self._feature_blocks		,	)

class      UpperCamelCase_							(				UpperCamelCase       ):




						def      snake_case__(    self       )			->						Any:
													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__  (   UpperCAmelCase     )     ->		Dict[str, str]:







							"""simple docstring"""





							_a  :    List[Any]			=						RegNetConfig(depths=[2, 7, 17, 1]    ,	hidden_sizes=[8, 8, 8, 8]    ,	groups_width=8     )
							if "in1k" in model_name:
														_a  :    List[str]			=						RegNetForImageClassification(UpperCAmelCase     )
							else:
														_a  :    Union[str, Any]			=						RegNetModel(UpperCAmelCase     )
							# create from model (with small weights)
							_a  :    Union[str, Any]			=						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():
														_a  :    Union[str, Any]			=						from_model.eval()
														_a  :    Optional[int]			=						our_model.eval()

														_a  :    Tuple			=						torch.randn((1, 3, 32, 32)     )
														# trace both
														_a  :    List[Any]			=						Tracker(UpperCAmelCase     )
														_a  :    Union[str, Any]			=						dest_tracker(UpperCAmelCase     ).parametrized

														pprint(dest_tracker.nameamodule     )
														_a  :    List[Any]			=						Tracker(UpperCAmelCase     )
														_a  :    Dict			=						src_tracker(UpperCAmelCase     ).parametrized

							# convert the keys -> module dict to keys -> params
							def to_params_dict(UpperCAmelCase     ):
														_a  :    Dict			=						OrderedDict()
														for name, module in dict_with_modules.items():
																					for param_name, param in module.state_dict().items():
																												_a  :    Union[str, Any]			=						param
														return params_dict

							_a  :    int			=						{}

							_a  :    List[str]			=						to_params_dict(UpperCAmelCase     )
							_a  :    int			=						to_params_dict(UpperCAmelCase     )

							for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()    ,	dst_state_dict.items()     ):
														_a  :    Optional[Any]			=						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:
														_a  :    Optional[Any]			=						'''classifier.1.weight'''
														_a  :    int			=						'''classifier.1.bias'''

							return from_to_ours_keys






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase = None    ,	UpperCAmelCase = True     )     ->		Tuple:







							"""simple docstring"""





							_a  :    int			=						'''imagenet-1k-id2label.json'''
							_a  :    Optional[Any]			=						1000

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

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

							_a  :    int			=						partial(UpperCAmelCase    ,	num_labels=UpperCAmelCase    ,	idalabel=UpperCAmelCase    ,	labelaid=UpperCAmelCase     )

							_a  :    Union[str, Any]			=						{
							    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]    ,	hidden_sizes=[2020, 4040, 11110, 28280]    ,	groups_width=1010     ),
							    # finetuned on imagenet
							    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]    ,	hidden_sizes=[2020, 4040, 11110, 28280]    ,	groups_width=1010     ),
							}

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

							_a  :    Union[str, Any]			=						{
							    '''regnet-y-10b-seer''': partial(
							        UpperCAmelCase    ,	'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''    ,	),
							    '''regnet-y-10b-seer-in1k''': partial(
							        UpperCAmelCase    ,	'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''    ,	),
							}

							_a  :    Union[str, Any]			=						get_from_to_our_keys(UpperCAmelCase     )

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

														_a  :    Optional[int]			=						{}

														_a  :    int			=						list(from_state_dict.keys()     )
														_a  :    Optional[Any]			=						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
																					_a  :    str			=						re.sub(UpperCAmelCase    ,	''''''    ,	UpperCAmelCase     )
																					# 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
																					_a  :    Any			=						from_to_ours_keys[src_key]
																					# store the parameter with our key
																					_a  :    Union[str, Any]			=						from_state_dict[key]
																					not_used_keys.remove(UpperCAmelCase     )
														# check that all keys have been updated
														assert len(UpperCAmelCase     ) == 0, F'Some keys where not used {",".join(UpperCAmelCase     )}'

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

														# save our state dict to disk
														torch.save(UpperCAmelCase    ,	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
														_a  :    Optional[Any]			=						names_to_config[model_name]
														_a  :    Optional[Any]			=						RegNetModel
														if "in1k" in model_name:
																					_a  :    Union[str, Any]			=						RegNetForImageClassification
														_a  :    Optional[Any]			=						our_model_func(UpperCAmelCase     )
														# 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
														_a  :    str			=						our_model.state_dict().keys()
														PreTrainedModel._load_pretrained_model_low_mem(
														    UpperCAmelCase    ,	UpperCAmelCase    ,	[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    ,	)
														_a  :    List[Any]			=						384
														# we can use the convnext one
														_a  :    str			=						AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''    ,	size=UpperCAmelCase     )
														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__":
						__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)







__lowerCamelCase						= {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

__lowerCamelCase						= {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		list:







							"""simple docstring"""





							_a  :    List[Any]			=						True
							_a  :    Optional[Any]			=						[]

							for neighbour in graph[vert]:
														if not visited[neighbour]:
																					connected_verts += dfs(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )

							return [vert, *connected_verts]






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		list:







							"""simple docstring"""





							_a  :    Optional[int]			=						len(UpperCAmelCase     )
							_a  :    Optional[Any]			=						graph_size * [False]
							_a  :    Optional[int]			=						[]

							for i in range(UpperCAmelCase     ):
														if not visited[i]:
																					_a  :    List[str]			=						dfs(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )
																					components_list.append(UpperCAmelCase     )

							return components_list


if __name__ == "__main__":
						import doctest

						doctest.testmod()