



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()
UpperCAmelCase__   =       logging.get_logger()







@dataclass
class 			__lowerCAmelCase    :
			UpperCamelCase   					=     4_2
			UpperCamelCase   					=     field(default_factory=snake_case_						)
			UpperCamelCase   					=     field(default_factory=snake_case_						)



			def     _lowerCamelCase       (						self   :  Union[str, Any] ,		A   :  str ,		A   :  List[str] ,		A   :  Dict)   ->    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   :  str ,		A   :  List[str])   ->    int:



						"""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     _lowerCamelCase       (						self   :  Tuple)   ->    List[Any]:



						"""simple docstring"""






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







@dataclass
class 			__lowerCAmelCase    :
			UpperCamelCase   					=     4_2
			UpperCamelCase   					=     4_2
			UpperCamelCase   					=     1
			UpperCamelCase   					=     field(default_factory=snake_case_						)
			UpperCamelCase   					=     field(default_factory=snake_case_						)
			UpperCamelCase   					=     True



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



						"""simple docstring"""






						_UpperCAmelCase		       =   Tracker(self.dest)(A).parametrized
						_UpperCAmelCase		       =   Tracker(self.src)(A).parametrized

						_UpperCAmelCase		       =   list(filter(lambda A: type(A) not in self.src_skip ,		A))
						_UpperCAmelCase		       =   list(filter(lambda A: 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 			__lowerCAmelCase    (			nn.Module						):



			def __init__(						self   :  Tuple ,		A   :  Any)   ->    List[str]:



						"""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     _lowerCamelCase       (						self   :  List[str] ,		A   :  Optional[Any])   ->    Tuple:



						"""simple docstring"""






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







class 			__lowerCAmelCase    (			snake_case_						):



			def     _lowerCamelCase       (						self   :  str ,		A   :  int)   ->    str:



						"""simple docstring"""






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



			def __getitem__(						self   :  Optional[Any] ,		A   :  Dict)   ->    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 			__lowerCAmelCase    (			snake_case_						):



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



						"""simple docstring"""






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





def 							A	(     _UpperCAmelCase				:   int							,   _UpperCAmelCase				:   int							,   _UpperCAmelCase				:   List[Tuple[str, str]]    )				-> Tuple:







			'''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 							A	(     _UpperCAmelCase				:   str							,   _UpperCAmelCase				:   Callable[[], nn.Module]							,   _UpperCAmelCase				:   Callable[[], nn.Module]							,   _UpperCAmelCase				:   RegNetConfig							,   _UpperCAmelCase				:   Path							,   _UpperCAmelCase				:   bool = True							,   )				-> Dict:







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

			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(__lowerCamelCase							,   our_model.state_dict()							,   __lowerCamelCase    )
						our_model.load_state_dict(__lowerCamelCase    )

			_UpperCAmelCase		       =   our_model(__lowerCamelCase							,   output_hidden_states=__lowerCamelCase    )
			_UpperCAmelCase		       =   (
			    our_outputs.logits if isinstance(__lowerCamelCase							,   __lowerCamelCase    ) else our_outputs.last_hidden_state
			)

			_UpperCAmelCase		       =   from_model(__lowerCamelCase    )
			_UpperCAmelCase		       =   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:
						_UpperCAmelCase		       =   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							,   )

						_UpperCAmelCase		       =   224 if 'seer' not in name else 384
						# we can use the convnext one
						_UpperCAmelCase		       =   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 							A	(     _UpperCAmelCase				:   Path							,   _UpperCAmelCase				:   str = None							,   _UpperCAmelCase				:   bool = True    )				-> List[Any]:







			'''simple docstring'''
			_UpperCAmelCase		       =   'imagenet-1k-id2label.json'
			_UpperCAmelCase		       =   1_000
			_UpperCAmelCase		       =   (1, num_labels)

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

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

			_UpperCAmelCase		       =   partial(__lowerCamelCase							,   num_labels=__lowerCamelCase							,   idalabel=__lowerCamelCase							,   labelaid=__lowerCamelCase    )

			_UpperCAmelCase		       =   {
			    '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    ),
			}

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

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

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

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

			_UpperCAmelCase		       =   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()    )							,   )

			_UpperCAmelCase		       =   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
			_UpperCAmelCase		       =   partial(
			    __lowerCamelCase							,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'							,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )							,   )

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

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

			_UpperCAmelCase		       =   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__":
							UpperCAmelCase__   =       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__   =       parser.parse_args()

							UpperCAmelCase__   =       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'''

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowercase_					= HUGGINGFACE_HUB_CACHE


lowercase_					= """config.json"""
lowercase_					= """diffusion_pytorch_model.bin"""
lowercase_					= """diffusion_flax_model.msgpack"""
lowercase_					= """model.onnx"""
lowercase_					= """diffusion_pytorch_model.safetensors"""
lowercase_					= """weights.pb"""
lowercase_					= """https://huggingface.co"""
lowercase_					= default_cache_path
lowercase_					= """diffusers_modules"""
lowercase_					= os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowercase_					= ["""fp16""", """non-ema"""]
lowercase_					= """.self_attn"""



