






"""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()
A__	:    Tuple			 = logging.get_logger()







@dataclass
class        lowercase__      :
			_UpperCAmelCase		:nn.Module
			_UpperCAmelCase		:List[nn.Module]             =			field(default_factory=snake_case__       )
			_UpperCAmelCase		:list             =			field(default_factory=snake_case__       )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  str       ,	snake_case__					:  Tensor       ,	snake_case__					:  Tensor ):
										lowerCamelCase_	:							Union[str, 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[str]       ,	snake_case__					:  Tensor ):
										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[int] ):
										# check the len of the state_dict keys to see if we have learnable params
										return list(filter(lambda snake_case__ : len(list(x.state_dict().keys() ) ) > 0       ,	self.traced ) )







@dataclass
class        lowercase__      :
			_UpperCAmelCase		:nn.Module
			_UpperCAmelCase		:nn.Module
			_UpperCAmelCase		:int             =			1
			_UpperCAmelCase		:List             =			field(default_factory=snake_case__       )
			_UpperCAmelCase		:List             =			field(default_factory=snake_case__       )
			_UpperCAmelCase		:bool             =			True


			def __call__(				self					:  Union[str, Any]       ,	snake_case__					:  Tensor ):
										lowerCamelCase_	:							int									=Tracker(self.dest )(snake_case__ ).parametrized
										lowerCamelCase_	:							Union[str, Any]									=Tracker(self.src )(snake_case__ ).parametrized

										lowerCamelCase_	:							Tuple									=list(filter(lambda snake_case__ : type(snake_case__ ) not in self.src_skip       ,	snake_case__ ) )
										lowerCamelCase_	:							List[str]									=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        lowercase__      (							nn.Module       ):


			def __init__(				self					:  Union[str, Any]       ,	snake_case__					:  nn.Module ):
										super().__init__()

										lowerCamelCase_	:							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}"""
																	lowerCamelCase_	:							Any									=len(snake_case__ ) + 1
																	feature_blocks.append((F"""res{block_index}""", v) )

										lowerCamelCase_	:							Optional[Any]									=nn.ModuleDict(snake_case__ )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  Tensor ):
										return get_trunk_forward_outputs(
										    snake_case__       ,	out_feat_keys=snake_case__       ,	feature_blocks=self._feature_blocks       ,	)







class        lowercase__      (							snake_case__       ):


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  str ):
										lowerCamelCase_	:							List[Any]									=x.split("-" )
										return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )


			def __getitem__(				self					:  Union[str, Any]       ,	snake_case__					:  str ):
										# default to timm!
										if x not in self:
																	lowerCamelCase_	:							List[Any]									=self.convert_name_to_timm(snake_case__ )
																	lowerCamelCase_	:							List[str]									=partial(lambda: (timm.create_model(snake_case__       ,	pretrained=snake_case__ ).eval(), None) )

										else:
																	lowerCamelCase_	:							Any									=super().__getitem__(snake_case__ )

										return val







class        lowercase__      (							snake_case__       ):


			def __getitem__(				self					:  List[Any]       ,	snake_case__					:  str ):
										if "seer" in x and "in1k" not in x:
																	lowerCamelCase_	:							Dict									=RegNetModel
										else:
																	lowerCamelCase_	:							Dict									=RegNetForImageClassification
										return val


def 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   Any    ,						lowerCamelCase__     :   List[Tuple[str, str]]  )    ->     int:
							for from_key, to_key in keys:
														lowerCamelCase_	:							Tuple									=from_state_dict[from_key].clone()
														print(F"""Copied key={from_key} to={to_key}"""  )
							return to_state_dict

def 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   Callable[[], nn.Module]    ,						lowerCamelCase__     :   Callable[[], nn.Module]    ,						lowerCamelCase__     :   RegNetConfig    ,						lowerCamelCase__     :   Path    ,						lowerCamelCase__     :   bool = True    ,						)    ->     str:
							print(F"""Converting {name}..."""  )
							with torch.no_grad():
														lowerCamelCase_	:							Dict									=from_model_func()
														lowerCamelCase_	:							str									=our_model_func(lowerCamelCase__  ).eval()
														lowerCamelCase_	:							List[str]									=ModuleTransfer(src=lowerCamelCase__    ,						dest=lowerCamelCase__    ,						raise_if_mismatch=lowerCamelCase__  )
														lowerCamelCase_	:							Optional[int]									=torch.randn((1, 3, 224, 224)  )
														module_transfer(lowerCamelCase__  )

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

							lowerCamelCase_	:							Optional[Any]									=our_model(lowerCamelCase__    ,						output_hidden_states=lowerCamelCase__  )
							lowerCamelCase_	:							Any									=(
							    our_outputs.logits if isinstance(lowerCamelCase__    ,						lowerCamelCase__  ) else our_outputs.last_hidden_state
							)

							lowerCamelCase_	:							Dict									=from_model(lowerCamelCase__  )
							lowerCamelCase_	:							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:
														lowerCamelCase_	:							int									=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__    ,						)

														lowerCamelCase_	:							Tuple									=224 if "seer" not in name else 384
														# we can use the convnext one
														lowerCamelCase_	:							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 			_snake_case    (					lowerCamelCase__     :   Path    ,						lowerCamelCase__     :   str = None    ,						lowerCamelCase__     :   bool = True  )    ->     Optional[Any]:
							lowerCamelCase_	:							Union[str, Any]									="imagenet-1k-id2label.json"
							lowerCamelCase_	:							Any									=1_000
							lowerCamelCase_	:							Tuple									=(1, num_labels)

							lowerCamelCase_	:							List[str]									="huggingface/label-files"
							lowerCamelCase_	:							Union[str, Any]									=num_labels
							lowerCamelCase_	:							List[Any]									=json.load(open(cached_download(hf_hub_url(lowerCamelCase__    ,						lowerCamelCase__    ,						repo_type="dataset"  )  )    ,						"r"  )  )
							lowerCamelCase_	:							List[Any]									={int(lowerCamelCase__  ): v for k, v in idalabel.items()}

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

							lowerCamelCase_	:							List[Any]									=partial(lowerCamelCase__    ,						num_labels=lowerCamelCase__    ,						idalabel=lowerCamelCase__    ,						labelaid=lowerCamelCase__  )

							lowerCamelCase_	:							Union[str, Any]									={
							    "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, 1_008]    ,						groups_width=48    ,						layer_type="x"  ),
							    "regnet-x-040": ImageNetPreTrainedConfig(
							        depths=[2, 5, 14, 2]    ,						hidden_sizes=[80, 240, 560, 1_360]    ,						groups_width=40    ,						layer_type="x"  ),
							    "regnet-x-064": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]    ,						hidden_sizes=[168, 392, 784, 1_624]    ,						groups_width=56    ,						layer_type="x"  ),
							    "regnet-x-080": ImageNetPreTrainedConfig(
							        depths=[2, 5, 15, 1]    ,						hidden_sizes=[80, 240, 720, 1_920]    ,						groups_width=120    ,						layer_type="x"  ),
							    "regnet-x-120": ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]    ,						hidden_sizes=[224, 448, 896, 2_240]    ,						groups_width=112    ,						layer_type="x"  ),
							    "regnet-x-160": ImageNetPreTrainedConfig(
							        depths=[2, 6, 13, 1]    ,						hidden_sizes=[256, 512, 896, 2_048]    ,						groups_width=128    ,						layer_type="x"  ),
							    "regnet-x-320": ImageNetPreTrainedConfig(
							        depths=[2, 7, 13, 1]    ,						hidden_sizes=[336, 672, 1_344, 2_520]    ,						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, 1_512]    ,						groups_width=24  ),
							    "regnet-y-040": ImageNetPreTrainedConfig(
							        depths=[2, 6, 12, 2]    ,						hidden_sizes=[128, 192, 512, 1_088]    ,						groups_width=64  ),
							    "regnet-y-064": ImageNetPreTrainedConfig(
							        depths=[2, 7, 14, 2]    ,						hidden_sizes=[144, 288, 576, 1_296]    ,						groups_width=72  ),
							    "regnet-y-080": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]    ,						hidden_sizes=[168, 448, 896, 2_016]    ,						groups_width=56  ),
							    "regnet-y-120": ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]    ,						hidden_sizes=[224, 448, 896, 2_240]    ,						groups_width=112  ),
							    "regnet-y-160": ImageNetPreTrainedConfig(
							        depths=[2, 4, 11, 1]    ,						hidden_sizes=[224, 448, 1_232, 3_024]    ,						groups_width=112  ),
							    "regnet-y-320": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]    ,						hidden_sizes=[232, 696, 1_392, 3_712]    ,						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, 1_392, 3_712]    ,						groups_width=232  ),
							    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]    ,						hidden_sizes=[328, 984, 1_968, 4_920]    ,						groups_width=328  ),
							    "regnet-y-1280-seer": RegNetConfig(
							        depths=[2, 7, 17, 1]    ,						hidden_sizes=[528, 1_056, 2_904, 7_392]    ,						groups_width=264  ),
							    "regnet-y-2560-seer": RegNetConfig(
							        depths=[3, 7, 16, 1]    ,						hidden_sizes=[640, 1_696, 2_544, 5_088]    ,						groups_width=640  ),
							    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]    ,						hidden_sizes=[2_020, 4_040, 11_110, 28_280]    ,						groups_width=1_010  ),
							    # finetuned on imagenet
							    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]    ,						hidden_sizes=[232, 696, 1_392, 3_712]    ,						groups_width=232  ),
							    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]    ,						hidden_sizes=[328, 984, 1_968, 4_920]    ,						groups_width=328  ),
							    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]    ,						hidden_sizes=[528, 1_056, 2_904, 7_392]    ,						groups_width=264  ),
							    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[3, 7, 16, 1]    ,						hidden_sizes=[640, 1_696, 2_544, 5_088]    ,						groups_width=640  ),
							    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]    ,						hidden_sizes=[2_020, 4_040, 11_110, 28_280]    ,						groups_width=1_010  ),
							}

							lowerCamelCase_	:							Any									=NameToOurModelFuncMap()
							lowerCamelCase_	:							str									=NameToFromModelFuncMap()
							# add seer weights logic

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

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

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

							lowerCamelCase_	:							Optional[Any]									=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()  )    ,						)

							lowerCamelCase_	:							List[str]									=partial(
							    lowerCamelCase__    ,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"    ,						lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27    ,						group_width=1_010    ,						w_a=1_744    ,						w_a=620.83    ,						w_m=2.52  )  )  )    ,						)

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

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

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

							lowerCamelCase_	:							List[Any]									=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=1_010    ,						w_a=1_744    ,						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__	:    Optional[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.',
						)

						A__	:    Optional[Any]			 = parser.parse_args()

						A__	:    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)











"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   int  )    ->     int:

							if not isinstance(lowerCamelCase__    ,						lowerCamelCase__  ):
														raise TypeError("only integers accepted as input"  )
							else:
														lowerCamelCase_	:							str									=str(abs(lowerCamelCase__  )  )
														lowerCamelCase_	:							Tuple									=[list(lowerCamelCase__  ) for char in range(len(lowerCamelCase__  )  )]
														for index in range(len(lowerCamelCase__  )  ):
																					num_transpositions[index].pop(lowerCamelCase__  )
														return max(
														    int("".join(list(lowerCamelCase__  )  )  ) for transposition in num_transpositions  )


if __name__ == "__main__":
						__import__('doctest').testmod()



