





from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


UpperCAmelCase						:       Any        =					TypeVar('''DatasetType''', Dataset, IterableDataset)







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				DatasetType:
					if isinstance(a ,						a  ):
										return _split_by_node_map_style_dataset(a ,						rank=a ,						world_size=a  )
					else:
										return _split_by_node_iterable_dataset(a ,						rank=a ,						world_size=a  )











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()
UpperCAmelCase						:       Tuple        =					logging.get_logger()





@dataclass
class 						_A:


				"""simple docstring"""


				UpperCamelCase   :  nn.Module
				UpperCamelCase   :  List[nn.Module]    =     field(default_factory=snake_case__       )
				UpperCamelCase   :  list    =     field(default_factory=snake_case__       )
				UpperCamelCase   :  Dict[str, nn.Module]    =     field(default_factory=snake_case__       )



				def  UpperCAmelCase_		(      self					, _A					, _A					, _A					, _A					):
									__A     :						Tuple          =    len(list(m.modules()					)					) == 1 or isinstance(_A					, nn.Convad					) or isinstance(_A					, nn.BatchNormad					)
									if has_not_submodules:
														self.traced.append(_A					)
														__A     :						str          =    m



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





				@property
				def  UpperCAmelCase_		(      self					):
									# 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 						_A( nn.Module       ):


				"""simple docstring"""


				def __init__(      self					, _A					):
									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     :						Any          =    len(_A					) + 1
														feature_blocks.append((F"""res{block_index}""", v)					)

									__A     :						List[str]          =    nn.ModuleDict(_A					)





				def  UpperCAmelCase_		(      self					, _A					):
									return get_trunk_forward_outputs(
									    _A					, out_feat_keys=_A					, feature_blocks=self._feature_blocks					, )





class 						_A( snake_case__       ):


				"""simple docstring"""


				def  UpperCAmelCase_		(      self					):
									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 			_SCREAMING_SNAKE_CASE    (       a  )				->				Dict[str, str]:
					__A     :						int          =    RegNetConfig(depths=[2, 7, 17, 1] ,						hidden_sizes=[8, 8, 8, 8] ,						groups_width=8  )
					if "in1k" in model_name:
										__A     :						Any          =    RegNetForImageClassification(a  )
					else:
										__A     :						Tuple          =    RegNetModel(a  )
					# create from model (with small weights)
					__A     :						Union[str, Any]          =    FakeRegNetVisslWrapper(
					    RegNet(FakeRegNetParams(depth=27 ,						group_width=10_10 ,						w_a=17_44 ,						w_a=620.83 ,						w_m=2.52  )  )  )

					with torch.no_grad():
										__A     :						List[str]          =    from_model.eval()
										__A     :						Tuple          =    our_model.eval()

										__A     :						str          =    torch.randn((1, 3, 32, 32)  )
										# trace both
										__A     :						Union[str, Any]          =    Tracker(a  )
										__A     :						Optional[int]          =    dest_tracker(a  ).parametrized

										pprint(dest_tracker.nameamodule  )
										__A     :						Optional[int]          =    Tracker(a  )
										__A     :						Union[str, Any]          =    src_tracker(a  ).parametrized

					# convert the keys -> module dict to keys -> params
					def to_params_dict(a  ):
										__A     :						Optional[Any]          =    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     :						Dict          =    {}

					__A     :						Dict          =    to_params_dict(a  )
					__A     :						Any          =    to_params_dict(a  )

					for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items() ,						dst_state_dict.items()  ):
										__A     :						List[str]          =    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     :						Dict          =    'classifier.1.bias'

					return from_to_ours_keys







def 			_SCREAMING_SNAKE_CASE    (       a ,						a = None ,						a = True  )				->				Union[str, Any]:
					__A     :						Any          =    'imagenet-1k-id2label.json'
					__A     :						Optional[int]          =    10_00

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

					__A     :						Optional[int]          =    idalabel
					__A     :						Any          =    {v: k for k, v in idalabel.items()}

					__A     :						List[Any]          =    partial(a ,						num_labels=a ,						idalabel=a ,						labelaid=a  )

					__A     :						Optional[int]          =    {
					    '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-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  ),
					}

					# add seer weights logic
					def load_using_classy_vision(a  ) -> Tuple[Dict, Dict]:
										__A     :						Optional[Any]          =    torch.hub.load_state_dict_from_url(a ,						model_dir=str(a  ) ,						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     :						Dict          =    {
					    'regnet-y-10b-seer': partial(
					        a ,						'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch' ,						),
					    'regnet-y-10b-seer-in1k': partial(
					        a ,						'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch' ,						),
					}

					__A     :						Dict          =    get_from_to_our_keys(a  )

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

										__A     :						Optional[Any]          =    {}

										__A     :						Tuple          =    list(from_state_dict.keys()  )
										__A     :						List[str]          =    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     :						Optional[int]          =    re.sub(a ,						'' ,						a  )
															# 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     :						Union[str, 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(a  )
										# check that all keys have been updated
										assert len(a  ) == 0, F"""Some keys where not used {",".join(a  )}"""

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

										# save our state dict to disk
										torch.save(a ,						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[int]          =    names_to_config[model_name]
										__A     :						Any          =    RegNetModel
										if "in1k" in model_name:
															__A     :						Dict          =    RegNetForImageClassification
										__A     :						str          =    our_model_func(a  )
										# 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     :						List[Any]          =    our_model.state_dict().keys()
										PreTrainedModel._load_pretrained_model_low_mem(
										    a ,						a ,						[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     :						Union[str, Any]          =    3_84
										# we can use the convnext one
										__A     :						Union[str, Any]          =    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' ,						size=a  )
										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__":
				UpperCAmelCase						:       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.''',
				)

				UpperCAmelCase						:       Optional[int]        =					parser.parse_args()

				UpperCAmelCase						:       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)




