


'''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()
__snake_case							   =  logging.get_logger()
@dataclass
class      lowercase			:

		"""simple docstring"""

		_a									=       42
		_a									=       field(default_factory=A__      )
		_a									=       field(default_factory=A__      )




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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



@dataclass
class      lowercase			:

		"""simple docstring"""

		_a									=       42
		_a									=       42
		_a									=       1
		_a									=       field(default_factory=A__      )
		_a									=       field(default_factory=A__      )
		_a									=       True




		def __call__(	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				Tracker(self.dest							)(UpperCamelCase_							).parametrized
							UpperCamelCase__     :Dict							      =				Tracker(self.src							)(UpperCamelCase_							).parametrized

							UpperCamelCase__     :List[str]							      =				list(filter(lambda UpperCamelCase_							: type(UpperCamelCase_							) not in self.src_skip					,     UpperCamelCase_							)							)
							UpperCamelCase__     :Optional[int]							      =				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      lowercase			(							nn.Module      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_							):

							'''simple docstring'''


							super().__init__()

							UpperCamelCase__     :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}'''
												UpperCamelCase__     :List[Any]							      =				len(UpperCamelCase_							) + 1
												feature_blocks.append((F'''res{block_index}''', v)							)

							UpperCamelCase__     :List[Any]							      =				nn.ModuleDict(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


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



class      lowercase			(							A__      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


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




		def __getitem__(	self					,     UpperCamelCase_							):

							'''simple docstring'''


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

							else:
												UpperCamelCase__     :Tuple							      =				super().__getitem__(UpperCamelCase_							)

							return val



class      lowercase			(							A__      ):

		"""simple docstring"""

		def __getitem__(	self					,     UpperCamelCase_							):

							'''simple docstring'''


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



def     a   (    __a				,      __a				,      __a     )						->			str:



					'''simple docstring'''






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


def     a   (    __a				,      __a				,      __a				,      __a				,      __a				,      __a = True				,      )						->			Tuple:



					'''simple docstring'''






					print(f'''Converting {name}...'''     )
					with torch.no_grad():
										UpperCamelCase__      ,       UpperCamelCase__     :str							      =				from_model_func()
										UpperCamelCase__     :List[str]							      =				our_model_func(__a     ).eval()
										UpperCamelCase__     :Any							      =				ModuleTransfer(src=__a				,      dest=__a				,      raise_if_mismatch=__a     )
										UpperCamelCase__     :Optional[int]							      =				torch.randn((1, 3, 224, 224)     )
										module_transfer(__a     )

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

					UpperCamelCase__     :Dict							      =				our_model(__a				,      output_hidden_states=__a     )
					UpperCamelCase__     :Dict							      =				(
					    our_outputs.logits if isinstance(__a				,      __a     ) else our_outputs.last_hidden_state
					)

					UpperCamelCase__     :Tuple							      =				from_model(__a     )
					UpperCamelCase__     :str							      =				from_output[-1] if type(__a     ) 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__     :int							      =				our_outputs.hidden_states[-1]

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

										UpperCamelCase__     :Any							      =				224 if '''seer''' not in name else 384
										# we can use the convnext one
										UpperCamelCase__     :str							      =				AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''				,      size=__a     )
										image_processor.push_to_hub(
										    repo_path_or_name=save_directory / name				,      commit_message='''Add image processor'''				,      use_temp_dir=__a				,      )

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


def     a   (    __a				,      __a = None				,      __a = True     )						->			Dict:



					'''simple docstring'''






					UpperCamelCase__     :Any							      =				'''imagenet-1k-id2label.json'''
					UpperCamelCase__     :Union[str, Any]							      =				1000
					UpperCamelCase__     :List[Any]							      =				(1, num_labels)

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

					UpperCamelCase__     :Tuple							      =				idalabel
					UpperCamelCase__     :str							      =				{v: k for k, v in idalabel.items()}

					UpperCamelCase__     :List[str]							      =				partial(__a				,      num_labels=__a				,      idalabel=__a				,      labelaid=__a     )

					UpperCamelCase__     :int							      =				{
					    '''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, 11110, 28280]				,      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, 11110, 28280]				,      groups_width=1010     ),
					}

					UpperCamelCase__     :Tuple							      =				NameToOurModelFuncMap()
					UpperCamelCase__     :str							      =				NameToFromModelFuncMap()
					# add seer weights logic

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

					# pretrained
					UpperCamelCase__     :Union[str, Any]							      =				partial(
					    __a				,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''				,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )				,      )

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

					UpperCamelCase__     :Optional[Any]							      =				partial(
					    __a				,      '''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__     :Optional[Any]							      =				partial(
					    __a				,      '''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_2_0.8_3				,      w_m=2.5_2     )     )     )				,      )

					# IN1K finetuned
					UpperCamelCase__     :Optional[Any]							      =				partial(
					    __a				,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''				,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )				,      )

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

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

					UpperCamelCase__     :int							      =				partial(
					    __a				,      '''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_2_0.8_3				,      w_m=2.5_2     )     )     )				,      )

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


if __name__ == "__main__":
		__snake_case							   =  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.''',
		)

		__snake_case							   =  parser.parse_args()

		__snake_case							   =  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 tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch
@require_torch
@require_optimum
@slow
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :int							      =				AutoTokenizer.from_pretrained(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :int							      =				tokenizer('''This is me'''					,     return_tensors='''pt'''							)

							UpperCamelCase__     :Optional[int]							      =				model.to_bettertransformer()

							self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							UpperCamelCase__     :int							      =				model.generate(**UpperCamelCase_							)

							UpperCamelCase__     :Optional[Any]							      =				model.reverse_bettertransformer()

							self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

												self.assertFalse(
												    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()							)							)

												UpperCamelCase__     :Tuple							      =				model_reloaded.generate(**UpperCamelCase_							)
												self.assertTrue(torch.allclose(UpperCamelCase_					,     UpperCamelCase_							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :Dict							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
												with self.assertRaises(UpperCamelCase_							):
																	model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				model.reverse_bettertransformer()
												model.save_pretrained(UpperCamelCase_							)