





'''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__       : List[Any]         =       logging.get_logger()


@dataclass
class 					SCREAMING_SNAKE_CASE       :
     lowerCAmelCase						     =			42
     lowerCAmelCase						     =			field(default_factory=a__				)
     lowerCAmelCase						     =			field(default_factory=a__				)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Dict						=			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):

          '''simple docstring'''






          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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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




@dataclass
class 					SCREAMING_SNAKE_CASE       :
     lowerCAmelCase						     =			42
     lowerCAmelCase						     =			42
     lowerCAmelCase						     =			1
     lowerCAmelCase						     =			field(default_factory=a__				)
     lowerCAmelCase						     =			field(default_factory=a__				)
     lowerCAmelCase						     =			True


     def __call__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			Tracker(self.dest)(_UpperCAmelCase).parametrized
          __A		:					Any						=			Tracker(self.src)(_UpperCAmelCase).parametrized

          __A		:					int						=			list(filter(lambda _UpperCAmelCase: type(_UpperCAmelCase) not in self.src_skip		,      _UpperCAmelCase))
          __A		:					Tuple						=			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 					SCREAMING_SNAKE_CASE       (nn.Module				):


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          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		:					Union[str, Any]						=			len(_UpperCAmelCase) + 1
               feature_blocks.append((F'res{block_index}', v))

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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






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




class 					SCREAMING_SNAKE_CASE       (a__				):


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






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


     def __getitem__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          if x not in self:
               __A		:					Dict						=			self.convert_name_to_timm(_UpperCAmelCase)
               __A		:					Union[str, Any]						=			partial(lambda: (timm.create_model(_UpperCAmelCase		,      pretrained=_UpperCAmelCase).eval(), None))

          else:
               __A		:					Tuple						=			super().__getitem__(_UpperCAmelCase)

          return val




class 					SCREAMING_SNAKE_CASE       (a__				):


     def __getitem__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          if "seer" in x and "in1k" not in x:
               __A		:					Union[str, Any]						=			RegNetModel
          else:
               __A		:					Union[str, Any]						=			RegNetForImageClassification
          return val






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					Tuple					,				__snake_case    :					List[Tuple[str, str]]     )	->	Optional[int]:
     for from_key, to_key in keys:
          __A		:					Optional[int]						=			from_state_dict[from_key].clone()
          print(f'Copied key={from_key} to={to_key}'     )
     return to_state_dict






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					Callable[[], nn.Module]					,				__snake_case    :					Callable[[], nn.Module]					,				__snake_case    :					RegNetConfig					,				__snake_case    :					Path					,				__snake_case    :					bool = True					,				)	->	Any:
     print(f'Converting {name}...'     )
     with torch.no_grad():
          __A					,__A		:					Dict						=			from_model_func()
          __A		:					Optional[int]						=			our_model_func(__snake_case     ).eval()
          __A		:					Optional[int]						=			ModuleTransfer(src=__snake_case					,				dest=__snake_case					,				raise_if_mismatch=__snake_case     )
          __A		:					int						=			torch.randn((1, 3, 2_24, 2_24)     )
          module_transfer(__snake_case     )

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

     __A		:					Tuple						=			our_model(__snake_case					,				output_hidden_states=__snake_case     )
     __A		:					Optional[int]						=			(
         our_outputs.logits if isinstance(__snake_case					,				__snake_case     ) else our_outputs.last_hidden_state
     )

     __A		:					Union[str, Any]						=			from_model(__snake_case     )
     __A		:					int						=			from_output[-1] if type(__snake_case     ) 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:
          __A		:					List[Any]						=			our_outputs.hidden_states[-1]

     assert torch.allclose(__snake_case					,				__snake_case     ), "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=__snake_case					,				)

          __A		:					List[Any]						=			2_24 if 'seer' not in name else 3_84
          # we can use the convnext one
          __A		:					List[str]						=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'					,				size=__snake_case     )
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / name					,				commit_message='Add image processor'					,				use_temp_dir=__snake_case					,				)

          print(f'Pushed {name}'     )






def 			_lowerCAmelCase     (    __snake_case    :					Path					,				__snake_case    :					str = None					,				__snake_case    :					bool = True     )	->	Any:
     __A		:					List[Any]						=			'imagenet-1k-id2label.json'
     __A		:					int						=			10_00
     __A		:					int						=			(1, num_labels)

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

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

     __A		:					Optional[Any]						=			partial(__snake_case					,				num_labels=__snake_case					,				idalabel=__snake_case					,				labelaid=__snake_case     )

     __A		:					Union[str, Any]						=			{
         '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     ),
     }

     __A		:					List[Any]						=			NameToOurModelFuncMap()
     __A		:					List[str]						=			NameToFromModelFuncMap()
     # add seer weights logic

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

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

     __A		:					Optional[Any]						=			partial(
         __snake_case					,				'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'					,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )					,				)

     __A		:					int						=			partial(
         __snake_case					,				'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()     )					,				)

     __A		:					Any						=			partial(
         __snake_case					,				'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
     __A		:					Dict						=			partial(
         __snake_case					,				'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'					,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )					,				)

     __A		:					List[str]						=			partial(
         __snake_case					,				'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'					,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )					,				)

     __A		:					Optional[int]						=			partial(
         __snake_case					,				'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'					,				lambda: FakeRegNetVisslWrapper(RegNetYaaagf()     )					,				)

     __A		:					int						=			partial(
         __snake_case					,				'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(
              __snake_case					,				names_to_from_model_map[model_name]					,				names_to_ours_model_map[model_name]					,				names_to_config[model_name]					,				__snake_case					,				__snake_case					,				)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(
                   __snake_case					,				names_to_from_model_map[model_name]					,				names_to_ours_model_map[model_name]					,				__snake_case					,				__snake_case					,				__snake_case					,				)
     return config, expected_shape


if __name__ == "__main__":
  lowercase__       : Tuple         =       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__       : Optional[int]         =       parser.parse_args()

  lowercase__       : 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'''




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	Union[str, Any]:
     __A		:					int						=			RobertaPreLayerNormConfig.from_pretrained(
         __snake_case					,				architectures=['RobertaPreLayerNormForMaskedLM']     )

     # convert state_dict
     __A		:					Tuple						=			torch.load(hf_hub_download(repo_id=__snake_case					,				filename='pytorch_model.bin'     )     )
     __A		:					str						=			{}
     for tensor_key, tensor_value in original_state_dict.items():
          # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
          if tensor_key.startswith('roberta.'     ):
               __A		:					Dict						=			'roberta_prelayernorm.' + tensor_key[len('roberta.'     ) :]

          # The original implementation contains weights which are not used, remove them from the state_dict
          if tensor_key.endswith('.self.LayerNorm.weight'     ) or tensor_key.endswith('.self.LayerNorm.bias'     ):
               continue

          __A		:					str						=			tensor_value

     __A		:					Union[str, Any]						=			RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=__snake_case					,				config=__snake_case					,				state_dict=__snake_case     )
     model.save_pretrained(__snake_case     )

     # convert tokenizer
     __A		:					List[Any]						=			AutoTokenizer.from_pretrained(__snake_case     )
     tokenizer.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint-repo''',
      default=None,
      type=str,
      required=True,
      help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)