


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




@dataclass
class  a     :
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	field(default_factory=_SCREAMING_SNAKE_CASE       )
 _lowerCAmelCase 					=	field(default_factory=_SCREAMING_SNAKE_CASE       )
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Dict:
      _a			      =							len(list(m.modules()						)						) == 1 or isinstance(__magic_name__      ,				nn.Convad						) or isinstance(__magic_name__      ,				nn.BatchNormad						)
      if has_not_submodules:
           self.traced.append(__magic_name__						)
 def __call__( self      ,				__magic_name__						)					->							Tuple:
      for m in self.module.modules():
           self.handles.append(m.register_forward_hook(self._forward_hook						)						)
      self.module(__magic_name__						)
      [x.remove() for x in self.handles]
      return self




 @property
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda __magic_name__						: len(list(x.state_dict().keys()						)						) > 0      ,				self.traced						)						)





@dataclass
class  a     :
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	1
 _lowerCAmelCase 					=	field(default_factory=_SCREAMING_SNAKE_CASE       )
 _lowerCAmelCase 					=	field(default_factory=_SCREAMING_SNAKE_CASE       )
 _lowerCAmelCase 					=	True
 def __call__( self      ,				__magic_name__						)					->							Dict:
      _a			      =							Tracker(self.dest						)(__magic_name__						).parametrized
      _a			      =							Tracker(self.src						)(__magic_name__						).parametrized

      _a			      =							list(filter(lambda __magic_name__						: type(__magic_name__						) not in self.src_skip      ,				__magic_name__						)						)
      _a			      =							list(filter(lambda __magic_name__						: type(__magic_name__						) not in self.dest_skip      ,				__magic_name__						)						)

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

      for dest_m, src_m in zip(__magic_name__      ,				__magic_name__						):
           dest_m.load_state_dict(src_m.state_dict()						)
           if self.verbose == 1:
                print(f'Transfered from={src_m} to={dest_m}'						)





class  a     (     nn.Module       ):
 def __init__( self      ,				__magic_name__						)					->							Optional[int]:
      super().__init__()

      _a			      =							[]
      # - 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			      =							len(__magic_name__						) + 1
           feature_blocks.append((f'res{block_index}', v)						)

      _a			      =							nn.ModuleDict(__magic_name__						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[int]:
      return get_trunk_forward_outputs(
          __magic_name__      ,				out_feat_keys=__magic_name__      ,				feature_blocks=self._feature_blocks      ,				)





class  a     (     _SCREAMING_SNAKE_CASE       ):
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							str:
      _a			      =							x.split('-'						)
      return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]						)




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

      else:
           _a			      =							super().__getitem__(__magic_name__						)

      return val





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






def 	_A				(lowerCAmelCase__  :int				,						lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :List[Tuple[str, str]]		)   ->  int:




     '''simple docstring'''



     for from_key, to_key in keys:
          _a			      =							from_state_dict[from_key].clone()
          print(f'Copied key={from_key} to={to_key}'		)
     return to_state_dict






def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :Callable[[], nn.Module]				,						lowerCAmelCase__  :Callable[[], nn.Module]				,						lowerCAmelCase__  :RegNetConfig				,						lowerCAmelCase__  :Path				,						lowerCAmelCase__  :bool = True				,						)   ->  int:




     '''simple docstring'''



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

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

     _a			      =							our_model(lowerCAmelCase__				,						output_hidden_states=lowerCAmelCase__		)
     _a			      =							(
         our_outputs.logits if isinstance(lowerCAmelCase__				,						lowerCAmelCase__		) else our_outputs.last_hidden_state
     )

     _a			      =							from_model(lowerCAmelCase__		)
     _a			      =							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:
          _a			      =							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__				,						)

          _a			      =							2_24 if 'seer' not in name else 3_84
          # we can use the convnext one
          _a			      =							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				(lowerCAmelCase__  :Path				,						lowerCAmelCase__  :str = None				,						lowerCAmelCase__  :bool = True		)   ->  str:




     '''simple docstring'''



     _a			      =							'imagenet-1k-id2label.json'
     _a			      =							10_00
     _a			      =							(1, num_labels)

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

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

     _a			      =							partial(lowerCAmelCase__				,						num_labels=lowerCAmelCase__				,						idalabel=lowerCAmelCase__				,						labelaid=lowerCAmelCase__		)

     _a			      =							{
         '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			      =							NameToOurModelFuncMap()
     _a			      =							NameToFromModelFuncMap()
     # add seer weights logic

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

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

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

     _a			      =							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()		)				,						)

     _a			      =							partial(
         lowerCAmelCase__				,						'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=6_2_0.8_3				,						w_m=2.5_2		)		)		)				,						)

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

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

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

     _a			      =							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=10_10				,						w_a=17_44				,						w_a=6_2_0.8_3				,						w_m=2.5_2		)		)		)				,						)

     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_					:    Dict          =    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'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_					:    int          =    {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[Any]          =    ["ViTFeatureExtractor"]
 a_					:    Optional[int]          =    ["ViTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Optional[int]          =    [
     "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTForImageClassification",
     "ViTForMaskedImageModeling",
     "ViTModel",
     "ViTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Any          =    [
     "TFViTForImageClassification",
     "TFViTModel",
     "TFViTPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Dict          =    [
     "FlaxViTForImageClassification",
     "FlaxViTModel",
     "FlaxViTPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

 a_					:    List[str]          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

