



"""simple docstring"""





from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	''''''
       UpperCamelCase		=	'''hf-legacy'''  # "hf://"" is reserved for hffs



       def __init__(				self     :    Optional[Any]						,							_UpperCAmelCase     :    Optional[DatasetInfo] = None						,							_UpperCAmelCase     :    Optional[str] = None						,							**_UpperCAmelCase     :    List[Any]						,							)					->		List[str]:


             '''simple docstring'''




             super().__init__(self						,							**_UpperCAmelCase       )
             UpperCAmelCase_     					=				repo_info
             UpperCAmelCase_     					=				token
             UpperCAmelCase_     					=				None



       def 	lowercase__							(				self     :    List[Any]       )					->		Any:


             '''simple docstring'''




             if self.dir_cache is None:
                   UpperCAmelCase_     					=				{}
                   for hf_file in self.repo_info.siblings:
                         # TODO(QL): add sizes
                         UpperCAmelCase_     					=				{
                             "name": hf_file.rfilename,
                             "size": None,
                             "type": "file",
                         }
                         self.dir_cache.update(
                             {
                                 str(_UpperCAmelCase       ): {"name": str(_UpperCAmelCase       ), "size": None, "type": "directory"}
                                 for d in list(PurePosixPath(hf_file.rfilename       ).parents       )[:-1]
                             }       )



       def 	lowercase__							(				self     :    List[Any]						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    str = "rb"						,							**_UpperCAmelCase     :    Optional[int]						,							)					->		str:


             '''simple docstring'''




             if not isinstance(self.repo_info						,							_UpperCAmelCase       ):
                   raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}"""       )
             UpperCAmelCase_     					=				hf_hub_url(self.repo_info.id						,							_UpperCAmelCase						,							revision=self.repo_info.sha       )
             return fsspec.open(
                 _UpperCAmelCase						,							mode=_UpperCAmelCase						,							headers=get_authentication_headers_for_url(_UpperCAmelCase						,							use_auth_token=self.token       )						,							client_kwargs={"trust_env": True}						,							).open()



       def 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    List[str]						,							**_UpperCAmelCase     :    Tuple       )					->		Union[str, Any]:


             '''simple docstring'''




             self._get_dirs()
             UpperCAmelCase_     					=				self._strip_protocol(_UpperCAmelCase       )
             if path in self.dir_cache:
                   return self.dir_cache[path]
             else:
                   raise FileNotFoundError(_UpperCAmelCase       )







       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    Optional[Any]=False						,							**_UpperCAmelCase     :    str       )					->		str:


             '''simple docstring'''




             self._get_dirs()
             UpperCAmelCase_     					=				PurePosixPath(path.strip("/"       )       )
             UpperCAmelCase_     					=				{}
             for p, f in self.dir_cache.items():
                   UpperCAmelCase_     					=				PurePosixPath(p.strip("/"       )       )
                   UpperCAmelCase_     					=				p.parent
                   if root == path:
                         UpperCAmelCase_     					=				f
             UpperCAmelCase_     					=				list(paths.values()       )
             if detail:
                   return out
             else:
                   return sorted(f["name"] for f in out       )








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()
SCREAMING_SNAKE_CASE__     					=			logging.get_logger()



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






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     Tensor   ,  UpperCAmelCase				:     Tensor):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						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				:     Optional[int]   ,  UpperCAmelCase				:     Tensor):
				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 _snake_case		(						self				:     Optional[Any]):
				# check the len of the state_dict keys to see if we have learnable params
				return list(filter(lambda UpperCAmelCase: len(list(x.state_dict().keys())) > 0   ,  self.traced))




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






		def __call__(						self				:     List[str]   ,  UpperCAmelCase				:     Tensor):
				SCREAMING_SNAKE_CASE_				:str				     =						Tracker(self.dest)(UpperCAmelCase).parametrized
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						Tracker(self.src)(UpperCAmelCase).parametrized

				SCREAMING_SNAKE_CASE_				:str				     =						list(filter(lambda UpperCAmelCase: type(UpperCAmelCase) not in self.src_skip   ,  UpperCAmelCase))
				SCREAMING_SNAKE_CASE_				:Tuple				     =						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 						_UpperCAmelCase      (     nn.Module							):






		def __init__(						self				:     Optional[Any]   ,  UpperCAmelCase				:     nn.Module):
				super().__init__()

				SCREAMING_SNAKE_CASE_				: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}"
						SCREAMING_SNAKE_CASE_				:Any				     =						len(UpperCAmelCase) + 1
						feature_blocks.append((F"res{block_index}", v))

				SCREAMING_SNAKE_CASE_				:List[str]				     =						nn.ModuleDict(UpperCAmelCase)







		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     Tensor):
				return get_trunk_forward_outputs(
				    UpperCAmelCase   ,  out_feat_keys=UpperCAmelCase   ,  feature_blocks=self._feature_blocks   ,  )




class 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						x.split("-")
				return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])







		def __getitem__(						self				:     str   ,  UpperCAmelCase				:     str):
				# default to timm!
				if x not in self:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						self.convert_name_to_timm(UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Tuple				     =						partial(lambda: (timm.create_model(UpperCAmelCase   ,  pretrained=UpperCAmelCase).eval(), None))

				else:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						super().__getitem__(UpperCAmelCase)

				return val




class 						_UpperCAmelCase      (     lowercase							):






		def __getitem__(						self				:     Any   ,  UpperCAmelCase				:     str):
				if "seer" in x and "in1k" not in x:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						RegNetModel
				else:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						RegNetForImageClassification
				return val




def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




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


def     lowercase	(    a     , a     , a     , a     , a     , a = True     , ):



		'''simple docstring'''




		print(F"Converting {name}..." )
		with torch.no_grad():
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Any				     =						from_model_func()
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						our_model_func(a ).eval()
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						ModuleTransfer(src=a     , dest=a     , raise_if_mismatch=a )
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.randn((1, 3, 224, 224) )
				module_transfer(a )

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

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

		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						from_model(a )
		SCREAMING_SNAKE_CASE_				:int				     =						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:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						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     , )

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						224 if "seer" not in name else 384
				# we can use the convnext one
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						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     lowercase	(    a     , a = None     , a = True ):



		'''simple docstring'''




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

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

		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						idalabel
		SCREAMING_SNAKE_CASE_				:List[str]				     =						{v: k for k, v in idalabel.items()}

		SCREAMING_SNAKE_CASE_				:int				     =						partial(a     , num_labels=a     , idalabel=a     , labelaid=a )

		SCREAMING_SNAKE_CASE_				:str				     =						{
		    "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 ),
		}

		SCREAMING_SNAKE_CASE_				:List[str]				     =						NameToOurModelFuncMap()
		SCREAMING_SNAKE_CASE_				:Tuple				     =						NameToFromModelFuncMap()
		# add seer weights logic

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

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

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

		SCREAMING_SNAKE_CASE_				:int				     =						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() )     , )

		SCREAMING_SNAKE_CASE_				:Tuple				     =						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=620.83     , w_m=2.52 ) ) )     , )

		# IN1K finetuned
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						partial(
		    a     , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"     , lambda: FakeRegNetVisslWrapper(RegNetYaagf() )     , )

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

		SCREAMING_SNAKE_CASE_				:List[str]				     =						partial(
		    a     , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"     , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )     , )

		SCREAMING_SNAKE_CASE_				:List[Any]				     =						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=620.83     , w_m=2.52 ) ) )     , )

		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__":
		SCREAMING_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.",
		)

		SCREAMING_SNAKE_CASE__     					=			parser.parse_args()

		SCREAMING_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)






