


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      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					List[nn.Module]    					=						field(default_factory=_a )
					snake_case_		:					list    					=						field(default_factory=_a )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Tensor  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Optional[int]:




											'''simple docstring'''

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


					def __call__(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						str:




											'''simple docstring'''

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






					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						List[str]:




											'''simple docstring'''

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






@dataclass
class      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					nn.Module
					snake_case_		:					int    					=						1
					snake_case_		:					List    					=						field(default_factory=_a )
					snake_case_		:					List    					=						field(default_factory=_a )
					snake_case_		:					bool    					=						True


					def __call__(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		Tracker(self.dest    )(SCREAMING_SNAKE_CASE    ).parametrized
											__snake_case				=		Tracker(self.src    )(SCREAMING_SNAKE_CASE    ).parametrized

											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.src_skip  ,     SCREAMING_SNAKE_CASE    )    )
											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.dest_skip  ,     SCREAMING_SNAKE_CASE    )    )

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

											for dest_m, src_m in zip(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ):
																	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						:		Any  ,     SCREAMING_SNAKE_CASE						:		nn.Module    )      ->						Dict:




											'''simple docstring'''

											super().__init__()

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

											__snake_case				=		nn.ModuleDict(SCREAMING_SNAKE_CASE    )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Tuple:




											'''simple docstring'''

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






class      UpperCamelCase(	_a ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						str:




											'''simple docstring'''

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






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




											'''simple docstring'''

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

											else:
																	__snake_case				=		super().__getitem__(SCREAMING_SNAKE_CASE    )

											return val






class      UpperCamelCase(	_a ):


					def __getitem__(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						Callable[[], nn.Module]:




											'''simple docstring'''

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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[Any]:


						'''simple docstring'''






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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = True   ,						)    ->						Optional[Any]:


						'''simple docstring'''






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

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

						__snake_case				=		our_model(_lowerCAmelCase   ,						output_hidden_states=_lowerCAmelCase			)
						__snake_case				=		(
						    our_outputs.logits if isinstance(_lowerCAmelCase   ,						_lowerCAmelCase			) else our_outputs.last_hidden_state
						)

						__snake_case				=		from_model(_lowerCAmelCase			)
						__snake_case				=		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:
												__snake_case				=		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   ,						)

												__snake_case				=		224 if "seer" not in name else 384
												# we can use the convnext one
												__snake_case				=		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   ,						_lowerCAmelCase = None   ,						_lowerCAmelCase = True			)    ->						str:


						'''simple docstring'''






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

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

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

						__snake_case				=		partial(_lowerCAmelCase   ,						num_labels=_lowerCAmelCase   ,						idalabel=_lowerCAmelCase   ,						labelaid=_lowerCAmelCase			)

						__snake_case				=		{
						    "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, 1_1110, 2_8280]   ,						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, 1_1110, 2_8280]   ,						groups_width=1010			),
						}

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

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

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

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

						__snake_case				=		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()			)   ,						)

						__snake_case				=		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=6_20.83   ,						w_m=2.52			)			)			)   ,						)

						# IN1K finetuned
						__snake_case				=		partial(
						    _lowerCAmelCase   ,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"   ,						lambda: FakeRegNetVisslWrapper(RegNetYaagf()			)   ,						)

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

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

						__snake_case				=		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=6_20.83   ,						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				:       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.',
	)

	A				:       int      =  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)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A				:       List[str]      =  {
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A				:       str      =  [
	    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TimeSeriesTransformerForPrediction',
	    'TimeSeriesTransformerModel',
	    'TimeSeriesTransformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	A				:       str      =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





