
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   =     logging.get_logger()





@dataclass
class 	_lowerCAmelCase					:
				_lowercase					=42
				_lowercase					=field(default_factory=__a       )
				_lowercase					=field(default_factory=__a       )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Optional[Any]:
							lowerCAmelCase_						=	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				,				_UpperCamelCase     )				->      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      __a						(					self     )				->      str:
							# check the len of the state_dict keys to see if we have learnable params
							return list(filter(lambda _UpperCamelCase     : len(list(x.state_dict().keys()     )     ) > 0				,				self.traced     )     )





@dataclass
class 	_lowerCAmelCase					:
				_lowercase					=42
				_lowercase					=42
				_lowercase					=1
				_lowercase					=field(default_factory=__a       )
				_lowercase					=field(default_factory=__a       )
				_lowercase					=True




				def __call__(					self				,				_UpperCamelCase     )				->      List[Any]:
							lowerCAmelCase_						=	Tracker(self.dest     )(_UpperCamelCase     ).parametrized
							lowerCAmelCase_						=	Tracker(self.src     )(_UpperCamelCase     ).parametrized

							lowerCAmelCase_						=	list(filter(lambda _UpperCamelCase     : type(_UpperCamelCase     ) not in self.src_skip				,				_UpperCamelCase     )     )
							lowerCAmelCase_						=	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 	_lowerCAmelCase					(	nn.Module       ):




				def __init__(					self				,				_UpperCamelCase     )				->      Dict:
							super().__init__()

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

							lowerCAmelCase_						=	nn.ModuleDict(_UpperCamelCase     )




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





class 	_lowerCAmelCase					(	__a       ):




				def      __a						(					self				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	x.split("-"     )
							return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]     )




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

							else:
										lowerCAmelCase_						=	super().__getitem__(_UpperCamelCase     )

							return val





class 	_lowerCAmelCase					(	__a       ):




				def __getitem__(					self				,				_UpperCamelCase     )				->      Callable[[], nn.Module]:
							if "seer" in x and "in1k" not in x:
										lowerCAmelCase_						=	RegNetModel
							else:
										lowerCAmelCase_						=	RegNetForImageClassification
							return val






def    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Optional[Any]    ,							__lowerCAmelCase  :  List[Tuple[str, str]]				):




			"""simple docstring"""


			for from_key, to_key in keys:
						lowerCAmelCase_						=	from_state_dict[from_key].clone()
						print(F"""Copied key={from_key} to={to_key}"""				)
			return to_state_dict






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  Callable[[], nn.Module]    ,							__lowerCAmelCase  :  Callable[[], nn.Module]    ,							__lowerCAmelCase  :  RegNetConfig    ,							__lowerCAmelCase  :  Path    ,							__lowerCAmelCase  :  bool = True    ,							):




			"""simple docstring"""


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

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

			lowerCAmelCase_						=	our_model(__lowerCAmelCase    ,							output_hidden_states=__lowerCAmelCase				)
			lowerCAmelCase_						=	(
			    our_outputs.logits if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else our_outputs.last_hidden_state
			)

			lowerCAmelCase_						=	from_model(__lowerCAmelCase				)
			lowerCAmelCase_						=	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_						=	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_						=	224 if "seer" not in name else 384
						# we can use the convnext one
						lowerCAmelCase_						=	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    lowerCamelCase__				(				__lowerCAmelCase  :  Path    ,							__lowerCAmelCase  :  str = None    ,							__lowerCAmelCase  :  bool = True				):




			"""simple docstring"""


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

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

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

			lowerCAmelCase_						=	partial(__lowerCAmelCase    ,							num_labels=__lowerCAmelCase    ,							idalabel=__lowerCAmelCase    ,							labelaid=__lowerCAmelCase				)

			lowerCAmelCase_						=	{
			    "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, 11110, 28280]    ,							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, 11110, 28280]    ,							groups_width=1010				),
			}

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

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

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

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

			lowerCAmelCase_						=	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_						=	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
			lowerCAmelCase_						=	partial(
			    __lowerCAmelCase    ,							"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"    ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)    ,							)

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

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

			lowerCAmelCase_						=	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   =     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   =     parser.parse_args()

			_A   =     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)





import functools






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	len(__lowerCAmelCase				)
			lowerCAmelCase_						=	len(__lowerCAmelCase				)

			@functools.cache
			def min_distance(__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  int				) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						lowerCAmelCase_						=	int(worda[indexa] != worda[indexa]				)  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1    ,							__lowerCAmelCase				)    ,							1 + min_distance(__lowerCAmelCase    ,							indexa + 1				)    ,							diff + min_distance(indexa + 1    ,							indexa + 1				)    ,							)

			return min_distance(0    ,							0				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



