'''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()
_lowercase      :      Tuple            =					logging.get_logger()






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				nn.Module
				__magic_name__    :				List[nn.Module]  		=	field(default_factory=A__	)
				__magic_name__    :				list  		=	field(default_factory=A__	)




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			Union[str, Any]				,   lowerCAmelCase      :			str				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     len(list(m.modules()				)				) == 1 or isinstance(__A				,   nn.Convad				) or isinstance(__A				,   nn.BatchNormad				)
										if has_not_submodules:
																self.traced.append(__A				)




				def __call__(		self      :			Optional[Any]				,   lowerCAmelCase      :			List[Any]				)->					Union[str, Any]:

										"""simple docstring"""




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





				@property
				def 					a__(		self      :			List[str]				)->					int:

										"""simple docstring"""




										return list(filter(lambda lowerCAmelCase				: len(list(x.state_dict().keys()				)				) > 0				,   self.traced				)				)







@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				nn.Module
				__magic_name__    :				nn.Module
				__magic_name__    :				int  		=	1
				__magic_name__    :				List  		=	field(default_factory=A__	)
				__magic_name__    :				List  		=	field(default_factory=A__	)
				__magic_name__    :				bool  		=	True




				def __call__(		self      :			Optional[int]				,   lowerCAmelCase      :			Optional[int]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     Tracker(self.dest				)(__A				).parametrized
										UpperCAmelCase					    =     Tracker(self.src				)(__A				).parametrized

										UpperCAmelCase					    =     list(filter(lambda lowerCAmelCase				: type(__A				) not in self.src_skip				,   __A				)				)
										UpperCAmelCase					    =     list(filter(lambda lowerCAmelCase				: type(__A				) not in self.dest_skip				,   __A				)				)

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

										for dest_m, src_m in zip(__A				,   __A				):
																dest_m.load_state_dict(src_m.state_dict()				)
																if self.verbose == 1:
																						print(F"""Transfered from={src_m} to={dest_m}"""				)







class 	UpperCamelCase__(       nn.Module	):




				def __init__(		self      :			Dict				,   lowerCAmelCase      :			List[str]				)->					Any:

										"""simple docstring"""




										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(__A				) + 1
																feature_blocks.append((F"""res{block_index}""", v)				)

										UpperCAmelCase					    =     nn.ModuleDict(__A				)





				def 					a__(		self      :			List[str]				,   lowerCAmelCase      :			Any				)->					Any:

										"""simple docstring"""




										return get_trunk_forward_outputs(
										    __A				,   out_feat_keys=__A				,   feature_blocks=self._feature_blocks				,   )







class 	UpperCamelCase__(       A__	):




				def 					a__(		self      :			List[str]				,   lowerCAmelCase      :			Union[str, Any]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     x.split('''-'''				)
										return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]				)





				def __getitem__(		self      :			str				,   lowerCAmelCase      :			Optional[int]				)->					Callable[[], Tuple[nn.Module, Dict]]:

										"""simple docstring"""




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

										else:
																UpperCAmelCase					    =     super().__getitem__(__A				)

										return val







class 	UpperCamelCase__(       A__	):




				def __getitem__(		self      :			int				,   lowerCAmelCase      :			Optional[int]				)->					Callable[[], nn.Module]:

										"""simple docstring"""




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







def 				lowerCamelCase__				(	A       :      List[Any]				,       A       :      int				,       A       :      List[Tuple[str, str]]					):




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




						'''simple docstring'''






						print(f"""Converting {name}..."""					)
						with torch.no_grad():
												UpperCAmelCase					    =     from_model_func()
												UpperCAmelCase					    =     our_model_func(lowercase__					).eval()
												UpperCAmelCase					    =     ModuleTransfer(src=lowercase__				,       dest=lowercase__				,       raise_if_mismatch=lowercase__					)
												UpperCAmelCase					    =     torch.randn((1, 3, 2_24, 2_24)					)
												module_transfer(lowercase__					)

						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(lowercase__				,       our_model.state_dict()				,       lowercase__					)
												our_model.load_state_dict(lowercase__					)

						UpperCAmelCase					    =     our_model(lowercase__				,       output_hidden_states=lowercase__					)
						UpperCAmelCase					    =     (
						    our_outputs.logits if isinstance(lowercase__				,       lowercase__					) else our_outputs.last_hidden_state
						)

						UpperCAmelCase					    =     from_model(lowercase__					)
						UpperCAmelCase					    =     from_output[-1] if type(lowercase__					) 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(lowercase__				,       lowercase__					), "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=lowercase__				,       )

												UpperCAmelCase					    =     2_24 if """seer""" not in name else 3_84
												# we can use the convnext one
												UpperCAmelCase					    =     AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''				,       size=lowercase__					)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / name				,       commit_message='''Add image processor'''				,       use_temp_dir=lowercase__				,       )

												print(f"""Pushed {name}"""					)


def 				lowerCamelCase__				(	A       :      Path				,       A       :      str = None				,       A       :      bool = True					):




						'''simple docstring'''






						UpperCAmelCase					    =     """imagenet-1k-id2label.json"""
						UpperCAmelCase					    =     10_00
						UpperCAmelCase					    =     (1, num_labels)

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

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

						UpperCAmelCase					    =     partial(lowercase__				,       num_labels=lowercase__				,       idalabel=lowercase__				,       labelaid=lowercase__					)

						UpperCAmelCase					    =     {
						    """regnet-x-002""": ImageNetPreTrainedConfig(
						        depths=[1, 1, 4, 7]				,       hidden_sizes=[24, 56, 1_52, 3_68]				,       groups_width=8				,       layer_type='''x'''					),
						    """regnet-x-004""": ImageNetPreTrainedConfig(
						        depths=[1, 2, 7, 12]				,       hidden_sizes=[32, 64, 1_60, 3_84]				,       groups_width=16				,       layer_type='''x'''					),
						    """regnet-x-006""": ImageNetPreTrainedConfig(
						        depths=[1, 3, 5, 7]				,       hidden_sizes=[48, 96, 2_40, 5_28]				,       groups_width=24				,       layer_type='''x'''					),
						    """regnet-x-008""": ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 5]				,       hidden_sizes=[64, 1_28, 2_88, 6_72]				,       groups_width=16				,       layer_type='''x'''					),
						    """regnet-x-016""": ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 2]				,       hidden_sizes=[72, 1_68, 4_08, 9_12]				,       groups_width=24				,       layer_type='''x'''					),
						    """regnet-x-032""": ImageNetPreTrainedConfig(
						        depths=[2, 6, 15, 2]				,       hidden_sizes=[96, 1_92, 4_32, 10_08]				,       groups_width=48				,       layer_type='''x'''					),
						    """regnet-x-040""": ImageNetPreTrainedConfig(
						        depths=[2, 5, 14, 2]				,       hidden_sizes=[80, 2_40, 5_60, 13_60]				,       groups_width=40				,       layer_type='''x'''					),
						    """regnet-x-064""": ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]				,       hidden_sizes=[1_68, 3_92, 7_84, 16_24]				,       groups_width=56				,       layer_type='''x'''					),
						    """regnet-x-080""": ImageNetPreTrainedConfig(
						        depths=[2, 5, 15, 1]				,       hidden_sizes=[80, 2_40, 7_20, 19_20]				,       groups_width=1_20				,       layer_type='''x'''					),
						    """regnet-x-120""": ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]				,       hidden_sizes=[2_24, 4_48, 8_96, 22_40]				,       groups_width=1_12				,       layer_type='''x'''					),
						    """regnet-x-160""": ImageNetPreTrainedConfig(
						        depths=[2, 6, 13, 1]				,       hidden_sizes=[2_56, 5_12, 8_96, 20_48]				,       groups_width=1_28				,       layer_type='''x'''					),
						    """regnet-x-320""": ImageNetPreTrainedConfig(
						        depths=[2, 7, 13, 1]				,       hidden_sizes=[3_36, 6_72, 13_44, 25_20]				,       groups_width=1_68				,       layer_type='''x'''					),
						    # y variant
						    """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]				,       hidden_sizes=[24, 56, 1_52, 3_68]				,       groups_width=8					),
						    """regnet-y-004""": ImageNetPreTrainedConfig(
						        depths=[1, 3, 6, 6]				,       hidden_sizes=[48, 1_04, 2_08, 4_40]				,       groups_width=8					),
						    """regnet-y-006""": ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 4]				,       hidden_sizes=[48, 1_12, 2_56, 6_08]				,       groups_width=16					),
						    """regnet-y-008""": ImageNetPreTrainedConfig(
						        depths=[1, 3, 8, 2]				,       hidden_sizes=[64, 1_28, 3_20, 7_68]				,       groups_width=16					),
						    """regnet-y-016""": ImageNetPreTrainedConfig(
						        depths=[2, 6, 17, 2]				,       hidden_sizes=[48, 1_20, 3_36, 8_88]				,       groups_width=24					),
						    """regnet-y-032""": ImageNetPreTrainedConfig(
						        depths=[2, 5, 13, 1]				,       hidden_sizes=[72, 2_16, 5_76, 15_12]				,       groups_width=24					),
						    """regnet-y-040""": ImageNetPreTrainedConfig(
						        depths=[2, 6, 12, 2]				,       hidden_sizes=[1_28, 1_92, 5_12, 10_88]				,       groups_width=64					),
						    """regnet-y-064""": ImageNetPreTrainedConfig(
						        depths=[2, 7, 14, 2]				,       hidden_sizes=[1_44, 2_88, 5_76, 12_96]				,       groups_width=72					),
						    """regnet-y-080""": ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]				,       hidden_sizes=[1_68, 4_48, 8_96, 20_16]				,       groups_width=56					),
						    """regnet-y-120""": ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]				,       hidden_sizes=[2_24, 4_48, 8_96, 22_40]				,       groups_width=1_12					),
						    """regnet-y-160""": ImageNetPreTrainedConfig(
						        depths=[2, 4, 11, 1]				,       hidden_sizes=[2_24, 4_48, 12_32, 30_24]				,       groups_width=1_12					),
						    """regnet-y-320""": ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]				,       hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,       groups_width=2_32					),
						    # models created by SEER -> https://arxiv.org/abs/2202.08360
						    """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]				,       hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,       groups_width=2_32					),
						    """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]				,       hidden_sizes=[3_28, 9_84, 19_68, 49_20]				,       groups_width=3_28					),
						    """regnet-y-1280-seer""": RegNetConfig(
						        depths=[2, 7, 17, 1]				,       hidden_sizes=[5_28, 10_56, 29_04, 73_92]				,       groups_width=2_64					),
						    """regnet-y-2560-seer""": RegNetConfig(
						        depths=[3, 7, 16, 1]				,       hidden_sizes=[6_40, 16_96, 25_44, 50_88]				,       groups_width=6_40					),
						    """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-320-seer-in1k""": ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]				,       hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,       groups_width=2_32					),
						    """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]				,       hidden_sizes=[3_28, 9_84, 19_68, 49_20]				,       groups_width=3_28					),
						    """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]				,       hidden_sizes=[5_28, 10_56, 29_04, 73_92]				,       groups_width=2_64					),
						    """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
						        depths=[3, 7, 16, 1]				,       hidden_sizes=[6_40, 16_96, 25_44, 50_88]				,       groups_width=6_40					),
						    """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					),
						}

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

						def load_using_classy_vision(A       :      str				,       A       :      Callable[[], nn.Module]					) -> Tuple[nn.Module, Dict]:
												UpperCAmelCase					    =     torch.hub.load_state_dict_from_url(lowercase__				,       model_dir=str(lowercase__					)				,       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(lowercase__					)
												return model.eval(), model_state_dict["heads"]

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

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

						UpperCAmelCase					    =     partial(
						    lowercase__				,       '''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(
						    lowercase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''				,       lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=27				,       group_width=10_10				,       w_a=17_44				,       w_a=620.83				,       w_m=2.52					)					)					)				,       )

						# IN1K finetuned
						UpperCAmelCase					    =     partial(
						    lowercase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''				,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)				,       )

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

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

						UpperCAmelCase					    =     partial(
						    lowercase__				,       '''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=10_10				,       w_a=17_44				,       w_a=620.83				,       w_m=2.52					)					)					)				,       )

						if model_name:
												convert_weight_and_push(
												    lowercase__				,       names_to_from_model_map[model_name]				,       names_to_ours_model_map[model_name]				,       names_to_config[model_name]				,       lowercase__				,       lowercase__				,       )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    lowercase__				,       names_to_from_model_map[model_name]				,       names_to_ours_model_map[model_name]				,       lowercase__				,       lowercase__				,       lowercase__				,       )
						return config, expected_shape


if __name__ == "__main__":
	_lowercase      :      str            =					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.""",
	)

	_lowercase      :      Tuple            =					parser.parse_args()

	_lowercase      :      str            =					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 				lowerCamelCase__				(	A       :      int				,       A       :      int					):




						'''simple docstring'''






						while a != 0:
												UpperCAmelCase    ,    UpperCAmelCase					    =     b % a, a
						return b




def 				lowerCamelCase__				(	A       :      int				,       A       :      int					):




						'''simple docstring'''






						if gcd(A				,       A					) != 1:
												UpperCAmelCase					    =     f"""mod inverse of {a!r} and {m!r} does not exist"""
												raise ValueError(A					)
						UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     1, 0, a
						UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     0, 1, m
						while va != 0:
												UpperCAmelCase					    =     ua // va
												UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
						return ua % m





