


"""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()
UpperCAmelCase       :	Union[str, Any]  		=						logging.get_logger()





@dataclass
class 	lowerCamelCase__  :







					"""simple docstring"""


					__a      			=				42
					__a      			=				field(default_factory=__snake_case							)
					__a      			=				field(default_factory=__snake_case							)






					def lowerCamelCase__				(			self       :					Optional[int]					,			UpperCamelCase       :					int					,			UpperCamelCase       :					str					,			UpperCamelCase       :					str    ):
												'''simple docstring'''






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






					def __call__(			self       :					Union[str, Any]					,			UpperCamelCase       :					Tuple    ):
												'''simple docstring'''






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






					@property
					def lowerCamelCase__				(			self       :					Optional[Any]    ):
												'''simple docstring'''






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






@dataclass
class 	lowerCamelCase__  :







					"""simple docstring"""


					__a      			=				42
					__a      			=				42
					__a      			=				1
					__a      			=				field(default_factory=__snake_case							)
					__a      			=				field(default_factory=__snake_case							)
					__a      			=				True






					def __call__(			self       :					Optional[Any]					,			UpperCamelCase       :					Dict    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Union[str, Any] 				=   Tracker(self.dest    )(a__    ).parametrized
												__UpperCAmelCase  :		Dict 				=   Tracker(self.src    )(a__    ).parametrized

												__UpperCAmelCase  :		List[str] 				=   list(filter(lambda UpperCamelCase    : type(a__    ) not in self.src_skip					,			a__    )    )
												__UpperCAmelCase  :		List[str] 				=   list(filter(lambda UpperCamelCase    : type(a__    ) not in self.dest_skip					,			a__    )    )

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

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






class 	lowerCamelCase__  (	nn.Module							):







					"""simple docstring"""


					def __init__(			self       :					Union[str, Any]					,			UpperCamelCase       :					Tuple    ):
												'''simple docstring'''






												super().__init__()

												__UpperCAmelCase  :		Tuple 				=   []
												# - 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  :		Any 				=   len(a__    ) + 1
																			feature_blocks.append((f'''res{block_index}''', v)    )

												__UpperCAmelCase  :		Dict 				=   nn.ModuleDict(a__    )






					def lowerCamelCase__				(			self       :					Optional[int]					,			UpperCamelCase       :					List[str]    ):
												'''simple docstring'''






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






class 	lowerCamelCase__  (	__snake_case							):







					"""simple docstring"""


					def lowerCamelCase__				(			self       :					Any					,			UpperCamelCase       :					List[Any]    ):
												'''simple docstring'''






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






					def __getitem__(			self       :					str					,			UpperCamelCase       :					Tuple    ):
												'''simple docstring'''






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

												else:
																			__UpperCAmelCase  :		str 				=   super().__getitem__(a__    )

												return val






class 	lowerCamelCase__  (	__snake_case							):







					"""simple docstring"""


					def __getitem__(			self       :					Any					,			UpperCamelCase       :					Tuple    ):
												'''simple docstring'''






												if "seer" in x and "in1k" not in x:
																			__UpperCAmelCase  :		List[str] 				=   RegNetModel
												else:
																			__UpperCAmelCase  :		List[str] 				=   RegNetForImageClassification
												return val






def 	lowerCamelCase			(	_UpperCamelCase						: List[str]			,     _UpperCamelCase						: Tuple			,     _UpperCamelCase						: List[Tuple[str, str]]				)						->			Tuple:

							'''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 	lowerCamelCase			(	_UpperCamelCase						: str			,     _UpperCamelCase						: Callable[[], nn.Module]			,     _UpperCamelCase						: Callable[[], nn.Module]			,     _UpperCamelCase						: RegNetConfig			,     _UpperCamelCase						: Path			,     _UpperCamelCase						: bool = True			,     )						->			Any:

							'''simple docstring'''



							print(f'''Converting {name}...'''				)
							with torch.no_grad():
														__UpperCAmelCase				,__UpperCAmelCase  :		Any 				=   from_model_func()
														__UpperCAmelCase  :		str 				=   our_model_func(UpperCamelCase__				).eval()
														__UpperCAmelCase  :		Optional[Any] 				=   ModuleTransfer(src=UpperCamelCase__			,     dest=UpperCamelCase__			,     raise_if_mismatch=UpperCamelCase__				)
														__UpperCAmelCase  :		Tuple 				=   torch.randn((1, 3, 2_2_4, 2_2_4)				)
														module_transfer(UpperCamelCase__				)

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

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

							__UpperCAmelCase  :		List[Any] 				=   from_model(UpperCamelCase__				)
							__UpperCAmelCase  :		Dict 				=   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  :		List[Any] 				=   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  :		Union[str, Any] 				=   2_2_4 if """seer""" not in name else 3_8_4
														# we can use the convnext one
														__UpperCAmelCase  :		List[Any] 				=   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			(	_UpperCamelCase						: Path			,     _UpperCamelCase						: str = None			,     _UpperCamelCase						: bool = True				)						->			Optional[int]:

							'''simple docstring'''



							__UpperCAmelCase  :		Tuple 				=   """imagenet-1k-id2label.json"""
							__UpperCAmelCase  :		int 				=   1_0_0_0
							__UpperCAmelCase  :		List[str] 				=   (1, num_labels)

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

							__UpperCAmelCase  :		List[Any] 				=   idalabel
							__UpperCAmelCase  :		Dict 				=   {v: k for k, v in idalabel.items()}

							__UpperCAmelCase  :		Optional[Any] 				=   partial(UpperCamelCase__			,     num_labels=UpperCamelCase__			,     idalabel=UpperCamelCase__			,     labelaid=UpperCamelCase__				)

							__UpperCAmelCase  :		Union[str, Any] 				=   {
							    """regnet-x-002""": ImageNetPreTrainedConfig(
							        depths=[1, 1, 4, 7]			,     hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]			,     groups_width=8			,     layer_type="""x"""				),
							    """regnet-x-004""": ImageNetPreTrainedConfig(
							        depths=[1, 2, 7, 1_2]			,     hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4]			,     groups_width=1_6			,     layer_type="""x"""				),
							    """regnet-x-006""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 5, 7]			,     hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8]			,     groups_width=2_4			,     layer_type="""x"""				),
							    """regnet-x-008""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 5]			,     hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2]			,     groups_width=1_6			,     layer_type="""x"""				),
							    """regnet-x-016""": ImageNetPreTrainedConfig(
							        depths=[2, 4, 1_0, 2]			,     hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2]			,     groups_width=2_4			,     layer_type="""x"""				),
							    """regnet-x-032""": ImageNetPreTrainedConfig(
							        depths=[2, 6, 1_5, 2]			,     hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8]			,     groups_width=4_8			,     layer_type="""x"""				),
							    """regnet-x-040""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_4, 2]			,     hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0]			,     groups_width=4_0			,     layer_type="""x"""				),
							    """regnet-x-064""": ImageNetPreTrainedConfig(
							        depths=[2, 4, 1_0, 1]			,     hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4]			,     groups_width=5_6			,     layer_type="""x"""				),
							    """regnet-x-080""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_5, 1]			,     hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0]			,     groups_width=1_2_0			,     layer_type="""x"""				),
							    """regnet-x-120""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_1, 1]			,     hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]			,     groups_width=1_1_2			,     layer_type="""x"""				),
							    """regnet-x-160""": ImageNetPreTrainedConfig(
							        depths=[2, 6, 1_3, 1]			,     hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8]			,     groups_width=1_2_8			,     layer_type="""x"""				),
							    """regnet-x-320""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_3, 1]			,     hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0]			,     groups_width=1_6_8			,     layer_type="""x"""				),
							    # y variant
							    """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]			,     hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]			,     groups_width=8				),
							    """regnet-y-004""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 6, 6]			,     hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0]			,     groups_width=8				),
							    """regnet-y-006""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 4]			,     hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8]			,     groups_width=1_6				),
							    """regnet-y-008""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 8, 2]			,     hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8]			,     groups_width=1_6				),
							    """regnet-y-016""": ImageNetPreTrainedConfig(
							        depths=[2, 6, 1_7, 2]			,     hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8]			,     groups_width=2_4				),
							    """regnet-y-032""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_3, 1]			,     hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2]			,     groups_width=2_4				),
							    """regnet-y-040""": ImageNetPreTrainedConfig(
							        depths=[2, 6, 1_2, 2]			,     hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]			,     groups_width=6_4				),
							    """regnet-y-064""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_4, 2]			,     hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6]			,     groups_width=7_2				),
							    """regnet-y-080""": ImageNetPreTrainedConfig(
							        depths=[2, 4, 1_0, 1]			,     hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6]			,     groups_width=5_6				),
							    """regnet-y-120""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_1, 1]			,     hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]			,     groups_width=1_1_2				),
							    """regnet-y-160""": ImageNetPreTrainedConfig(
							        depths=[2, 4, 1_1, 1]			,     hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4]			,     groups_width=1_1_2				),
							    """regnet-y-320""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_2, 1]			,     hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]			,     groups_width=2_3_2				),
							    # models created by SEER -> https://arxiv.org/abs/2202.08360
							    """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 1_2, 1]			,     hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]			,     groups_width=2_3_2				),
							    """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 1_2, 1]			,     hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]			,     groups_width=3_2_8				),
							    """regnet-y-1280-seer""": RegNetConfig(
							        depths=[2, 7, 1_7, 1]			,     hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]			,     groups_width=2_6_4				),
							    """regnet-y-2560-seer""": RegNetConfig(
							        depths=[3, 7, 1_6, 1]			,     hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]			,     groups_width=6_4_0				),
							    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_7, 1]			,     hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]			,     groups_width=1_0_1_0				),
							    # finetuned on imagenet
							    """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_2, 1]			,     hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]			,     groups_width=2_3_2				),
							    """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_2, 1]			,     hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]			,     groups_width=3_2_8				),
							    """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_7, 1]			,     hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]			,     groups_width=2_6_4				),
							    """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[3, 7, 1_6, 1]			,     hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]			,     groups_width=6_4_0				),
							    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_7, 1]			,     hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]			,     groups_width=1_0_1_0				),
							}

							__UpperCAmelCase  :		Dict 				=   NameToOurModelFuncMap()
							__UpperCAmelCase  :		List[Any] 				=   NameToFromModelFuncMap()
							# add seer weights logic

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

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

							__UpperCAmelCase  :		Union[str, Any] 				=   partial(
							    UpperCamelCase__			,     """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""			,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)			,     )

							__UpperCAmelCase  :		Any 				=   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  :		List[Any] 				=   partial(
							    UpperCamelCase__			,     """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""			,     lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=2_7			,     group_width=1_0_1_0			,     w_a=1_7_4_4			,     w_a=6_2_0.8_3			,     w_m=2.52				)				)				)			,     )

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

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

							__UpperCAmelCase  :		Union[str, Any] 				=   partial(
							    UpperCamelCase__			,     """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""			,     lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)			,     )

							__UpperCAmelCase  :		Dict 				=   partial(
							    UpperCamelCase__			,     """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""			,     lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=2_7			,     group_width=1_0_1_0			,     w_a=1_7_4_4			,     w_a=6_2_0.8_3			,     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__":
							UpperCAmelCase       :	Union[str, 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.',
							)

							UpperCAmelCase       :	Union[str, Any]  		=						parser.parse_args()

							UpperCAmelCase       :	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 transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def 	_lowerCAmelCase					( )			-> str:





	"""simple docstring"""







	A             =					HfArgumentParser(UpperCamelCase__    )
	A             =					parser.parse_args_into_dataclasses()[0]
	A             =					TensorFlowBenchmark(args=UpperCamelCase__    )
	try:
		A             =					parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		A             =					"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
		A             =					""" """.join(str(UpperCamelCase__    ).split(""" """    )[:-1]    )
		A             =					""""""
		A             =					eval(str(UpperCamelCase__    ).split(""" """    )[-1]    )
		A             =					[]
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]    )
			else:
				wrong_args.append(UpperCamelCase__    )
		if len(UpperCamelCase__    ) > 0:
			A             =					full_error_msg + begin_error_msg + str(UpperCamelCase__    )
		raise ValueError(UpperCamelCase__    )
	benchmark.run()


if __name__ == "__main__":
				main()



