



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()
__lowerCamelCase     :							Optional[Any]  							=				logging.get_logger()





@dataclass
class  __magic_name__   :
		lowercase      :       nn.Module
		lowercase      :       List[nn.Module]          =field(default_factory=A__						)
		lowercase      :       list          =field(default_factory=A__						)






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     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      :     Tuple				,		UpperCamelCase__      :     List[Any]      )				->					Dict:
								'''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      :     str      )				->					Dict:
								'''simple docstring'''







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





@dataclass
class  __magic_name__   :
		lowercase      :       nn.Module
		lowercase      :       nn.Module
		lowercase      :       int          =1
		lowercase      :       List          =field(default_factory=A__						)
		lowercase      :       List          =field(default_factory=A__						)
		lowercase      :       bool          =True






		def __call__(						self      :     Tuple				,		UpperCamelCase__      :     str      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     Tracker(self.dest      )(UpperCamelCase__      ).parametrized
								UpperCAmelCase         =     Tracker(self.src      )(UpperCamelCase__      ).parametrized

								UpperCAmelCase         =     list(filter(lambda UpperCamelCase__      : type(UpperCamelCase__      ) not in self.src_skip				,		UpperCamelCase__      )      )
								UpperCAmelCase         =     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  __magic_name__   (							nn.Module						):






		def __init__(						self      :     Optional[Any]				,		UpperCamelCase__      :     Dict      )				->					Optional[Any]:
								'''simple docstring'''







								super().__init__()

								UpperCAmelCase         =     []
								# - get the stem
								feature_blocks.append(("conv1", model.stem)      )
								# - get all the feature blocks
								for k, v in model.trunk_output.named_children():
														assert k.startswith("block"      ), F'Unexpected layer name {k}'
														UpperCAmelCase         =     len(UpperCamelCase__      ) + 1
														feature_blocks.append((F'res{block_index}', v)      )

								UpperCAmelCase         =     nn.ModuleDict(UpperCamelCase__      )







		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		UpperCamelCase__      :     Optional[int]      )				->					List[str]:
								'''simple docstring'''







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





class  __magic_name__   (							A__						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     Any      )				->					Optional[Any]:
								'''simple docstring'''







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







		def __getitem__(						self      :     str				,		UpperCamelCase__      :     int      )				->					Optional[Any]:
								'''simple docstring'''







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

								else:
														UpperCAmelCase         =     super().__getitem__(UpperCamelCase__      )

								return val





class  __magic_name__   (							A__						):






		def __getitem__(						self      :     Any				,		UpperCamelCase__      :     Optional[Any]      )				->					str:
								'''simple docstring'''







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

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       Optional[Any]:






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

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_ = True    ,   ) ->       List[Any]:
						print(F'Converting {name}...'    )
						with torch.no_grad():
												UpperCAmelCase       ,		UpperCAmelCase         =     from_model_func()
												UpperCAmelCase         =     our_model_func(_UpperCamelCase    ).eval()
												UpperCAmelCase         =     ModuleTransfer(src=_UpperCamelCase    ,   dest=_UpperCamelCase    ,   raise_if_mismatch=_UpperCamelCase    )
												UpperCAmelCase         =     torch.randn((1, 3, 224, 224)    )
												module_transfer(_UpperCamelCase    )

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

						UpperCAmelCase         =     our_model(_UpperCamelCase    ,   output_hidden_states=_UpperCamelCase    )
						UpperCAmelCase         =     (
						    our_outputs.logits if isinstance(_UpperCamelCase    ,   _UpperCamelCase    ) else our_outputs.last_hidden_state
						)

						UpperCAmelCase         =     from_model(_UpperCamelCase    )
						UpperCAmelCase         =     from_output[-1] if type(_UpperCamelCase    ) is list else from_output

						# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
						if "seer" in name and "in1k" in name:
												UpperCAmelCase         =     our_outputs.hidden_states[-1]

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

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

												print(F'Pushed {name}'    )

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_ = None    ,   lowerCamelCase_ = True    ) ->       Dict:
						UpperCAmelCase         =     "imagenet-1k-id2label.json"
						UpperCAmelCase         =     1_000
						UpperCAmelCase         =     (1, num_labels)

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

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

						UpperCAmelCase         =     partial(_UpperCamelCase    ,   num_labels=_UpperCamelCase    ,   idalabel=_UpperCamelCase    ,   labelaid=_UpperCamelCase    )

						UpperCAmelCase         =     {
						    "regnet-x-002": ImageNetPreTrainedConfig(
						        depths=[1, 1, 4, 7]    ,   hidden_sizes=[24, 56, 152, 368]    ,   groups_width=8    ,   layer_type="x"    ),
						    "regnet-x-004": ImageNetPreTrainedConfig(
						        depths=[1, 2, 7, 12]    ,   hidden_sizes=[32, 64, 160, 384]    ,   groups_width=16    ,   layer_type="x"    ),
						    "regnet-x-006": ImageNetPreTrainedConfig(
						        depths=[1, 3, 5, 7]    ,   hidden_sizes=[48, 96, 240, 528]    ,   groups_width=24    ,   layer_type="x"    ),
						    "regnet-x-008": ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 5]    ,   hidden_sizes=[64, 128, 288, 672]    ,   groups_width=16    ,   layer_type="x"    ),
						    "regnet-x-016": ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 2]    ,   hidden_sizes=[72, 168, 408, 912]    ,   groups_width=24    ,   layer_type="x"    ),
						    "regnet-x-032": ImageNetPreTrainedConfig(
						        depths=[2, 6, 15, 2]    ,   hidden_sizes=[96, 192, 432, 1_008]    ,   groups_width=48    ,   layer_type="x"    ),
						    "regnet-x-040": ImageNetPreTrainedConfig(
						        depths=[2, 5, 14, 2]    ,   hidden_sizes=[80, 240, 560, 1_360]    ,   groups_width=40    ,   layer_type="x"    ),
						    "regnet-x-064": ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]    ,   hidden_sizes=[168, 392, 784, 1_624]    ,   groups_width=56    ,   layer_type="x"    ),
						    "regnet-x-080": ImageNetPreTrainedConfig(
						        depths=[2, 5, 15, 1]    ,   hidden_sizes=[80, 240, 720, 1_920]    ,   groups_width=120    ,   layer_type="x"    ),
						    "regnet-x-120": ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]    ,   hidden_sizes=[224, 448, 896, 2_240]    ,   groups_width=112    ,   layer_type="x"    ),
						    "regnet-x-160": ImageNetPreTrainedConfig(
						        depths=[2, 6, 13, 1]    ,   hidden_sizes=[256, 512, 896, 2_048]    ,   groups_width=128    ,   layer_type="x"    ),
						    "regnet-x-320": ImageNetPreTrainedConfig(
						        depths=[2, 7, 13, 1]    ,   hidden_sizes=[336, 672, 1_344, 2_520]    ,   groups_width=168    ,   layer_type="x"    ),
						    # y variant
						    "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]    ,   hidden_sizes=[24, 56, 152, 368]    ,   groups_width=8    ),
						    "regnet-y-004": ImageNetPreTrainedConfig(
						        depths=[1, 3, 6, 6]    ,   hidden_sizes=[48, 104, 208, 440]    ,   groups_width=8    ),
						    "regnet-y-006": ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 4]    ,   hidden_sizes=[48, 112, 256, 608]    ,   groups_width=16    ),
						    "regnet-y-008": ImageNetPreTrainedConfig(
						        depths=[1, 3, 8, 2]    ,   hidden_sizes=[64, 128, 320, 768]    ,   groups_width=16    ),
						    "regnet-y-016": ImageNetPreTrainedConfig(
						        depths=[2, 6, 17, 2]    ,   hidden_sizes=[48, 120, 336, 888]    ,   groups_width=24    ),
						    "regnet-y-032": ImageNetPreTrainedConfig(
						        depths=[2, 5, 13, 1]    ,   hidden_sizes=[72, 216, 576, 1_512]    ,   groups_width=24    ),
						    "regnet-y-040": ImageNetPreTrainedConfig(
						        depths=[2, 6, 12, 2]    ,   hidden_sizes=[128, 192, 512, 1_088]    ,   groups_width=64    ),
						    "regnet-y-064": ImageNetPreTrainedConfig(
						        depths=[2, 7, 14, 2]    ,   hidden_sizes=[144, 288, 576, 1_296]    ,   groups_width=72    ),
						    "regnet-y-080": ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]    ,   hidden_sizes=[168, 448, 896, 2_016]    ,   groups_width=56    ),
						    "regnet-y-120": ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]    ,   hidden_sizes=[224, 448, 896, 2_240]    ,   groups_width=112    ),
						    "regnet-y-160": ImageNetPreTrainedConfig(
						        depths=[2, 4, 11, 1]    ,   hidden_sizes=[224, 448, 1_232, 3_024]    ,   groups_width=112    ),
						    "regnet-y-320": ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]    ,   hidden_sizes=[232, 696, 1_392, 3_712]    ,   groups_width=232    ),
						    # models created by SEER -> https://arxiv.org/abs/2202.08360
						    "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]    ,   hidden_sizes=[232, 696, 1_392, 3_712]    ,   groups_width=232    ),
						    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]    ,   hidden_sizes=[328, 984, 1_968, 4_920]    ,   groups_width=328    ),
						    "regnet-y-1280-seer": RegNetConfig(
						        depths=[2, 7, 17, 1]    ,   hidden_sizes=[528, 1_056, 2_904, 7_392]    ,   groups_width=264    ),
						    "regnet-y-2560-seer": RegNetConfig(
						        depths=[3, 7, 16, 1]    ,   hidden_sizes=[640, 1_696, 2_544, 5_088]    ,   groups_width=640    ),
						    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]    ,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]    ,   groups_width=1_010    ),
						    # finetuned on imagenet
						    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]    ,   hidden_sizes=[232, 696, 1_392, 3_712]    ,   groups_width=232    ),
						    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]    ,   hidden_sizes=[328, 984, 1_968, 4_920]    ,   groups_width=328    ),
						    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]    ,   hidden_sizes=[528, 1_056, 2_904, 7_392]    ,   groups_width=264    ),
						    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[3, 7, 16, 1]    ,   hidden_sizes=[640, 1_696, 2_544, 5_088]    ,   groups_width=640    ),
						    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]    ,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]    ,   groups_width=1_010    ),
						}

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

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

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

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

						UpperCAmelCase         =     partial(
						    _UpperCamelCase    ,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"    ,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    )    ,   )

						UpperCAmelCase         =     partial(
						    _UpperCamelCase    ,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"    ,   lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=27    ,   group_width=1_010    ,   w_a=1_744    ,   w_a=620.83    ,   w_m=2.52    )    )    )    ,   )

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

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

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

						UpperCAmelCase         =     partial(
						    _UpperCamelCase    ,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"    ,   lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=27    ,   group_width=1_010    ,   w_a=1_744    ,   w_a=620.83    ,   w_m=2.52    )    )    )    ,   )

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


if __name__ == "__main__":
	__lowerCamelCase     :							List[Any]  							=				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.",
	)

	__lowerCamelCase     :							List[str]  							=				parser.parse_args()

	__lowerCamelCase     :							int  							=				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)








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  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "hf-internal-testing/tiny-random-t5"
								UpperCAmelCase         =     AutoTokenizer.from_pretrained(UpperCamelCase__      )
								UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer("This is me"				,		return_tensors="pt"      )

								UpperCAmelCase         =     model.to_bettertransformer()

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

								UpperCAmelCase         =     model.generate(**UpperCamelCase__      )

								UpperCAmelCase         =     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         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

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

														UpperCAmelCase         =     model_reloaded.generate(**UpperCamelCase__      )
														self.assertTrue(torch.allclose(UpperCamelCase__				,		UpperCamelCase__      )      )







		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "hf-internal-testing/tiny-random-t5"
								UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

								UpperCAmelCase         =     model.to_bettertransformer()

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

														UpperCAmelCase         =     model.reverse_bettertransformer()
														model.save_pretrained(UpperCamelCase__      )



