





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__				:



     '''simple docstring'''

     __snake_case					:			nn.Module
     __snake_case					:			List[nn.Module]				=    field(default_factory=lowerCAmelCase_     )
     __snake_case					:			list				=    field(default_factory=lowerCAmelCase_     )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Tensor		,lowerCamelCase__		:					Tensor )  ->	Dict:




           '''simple docstring'''


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






     def __call__(							self		:					Tuple		,lowerCamelCase__		:					Tensor )  ->	Optional[Any]:




           '''simple docstring'''


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






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	List[Any]:




           '''simple docstring'''


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

@dataclass
class 	UpperCamelCase__				:



     '''simple docstring'''

     __snake_case					:			nn.Module
     __snake_case					:			nn.Module
     __snake_case					:			int				=    1
     __snake_case					:			List				=    field(default_factory=lowerCAmelCase_     )
     __snake_case					:			List				=    field(default_factory=lowerCAmelCase_     )
     __snake_case					:			bool				=    True






     def __call__(							self		:					int		,lowerCamelCase__		:					Tensor )  ->	Any:




           '''simple docstring'''


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

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

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

           for dest_m, src_m in zip(lowerCamelCase__		,lowerCamelCase__ ):
                 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     ):



     '''simple docstring'''

     def __init__(							self		:					Any		,lowerCamelCase__		:					nn.Module )  ->	Any:




           '''simple docstring'''


           super().__init__()

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

           SCREAMING_SNAKE_CASE	=		nn.ModuleDict(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					Tensor )  ->	Union[str, Any]:




           '''simple docstring'''


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

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					str )  ->	str:




           '''simple docstring'''


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






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




           '''simple docstring'''


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

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

           return val

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __getitem__(							self		:					Tuple		,lowerCamelCase__		:					str )  ->	Callable[[], nn.Module]:




           '''simple docstring'''


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



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     int:


      '''simple docstring'''


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



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE = True				,		)				->     List[str]:


      '''simple docstring'''


      print(F"""Converting {name}..."""			)
      with torch.no_grad():
            SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		from_model_func()
            SCREAMING_SNAKE_CASE	=		our_model_func(_SCREAMING_SNAKE_CASE			).eval()
            SCREAMING_SNAKE_CASE	=		ModuleTransfer(src=_SCREAMING_SNAKE_CASE				,		dest=_SCREAMING_SNAKE_CASE				,		raise_if_mismatch=_SCREAMING_SNAKE_CASE			)
            SCREAMING_SNAKE_CASE	=		torch.randn((1, 3, 2_24, 2_24)			)
            module_transfer(_SCREAMING_SNAKE_CASE			)

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

      SCREAMING_SNAKE_CASE	=		our_model(_SCREAMING_SNAKE_CASE				,		output_hidden_states=_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		(
          our_outputs.logits if isinstance(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) else our_outputs.last_hidden_state
      )

      SCREAMING_SNAKE_CASE	=		from_model(_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		from_output[-1] if type(_SCREAMING_SNAKE_CASE			) 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	=		our_outputs.hidden_states[-1]

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

            SCREAMING_SNAKE_CASE	=		2_24 if """seer""" not in name else 3_84
            # we can use the convnext one
            SCREAMING_SNAKE_CASE	=		AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""				,		size=_SCREAMING_SNAKE_CASE			)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / name				,		commit_message="""Add image processor"""				,		use_temp_dir=_SCREAMING_SNAKE_CASE				,		)

            print(F"""Pushed {name}"""			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE = None				,		_SCREAMING_SNAKE_CASE = True			)				->     Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		"""imagenet-1k-id2label.json"""
      SCREAMING_SNAKE_CASE	=		10_00
      SCREAMING_SNAKE_CASE	=		(1, num_labels)

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

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

      SCREAMING_SNAKE_CASE	=		partial(_SCREAMING_SNAKE_CASE				,		num_labels=_SCREAMING_SNAKE_CASE				,		idalabel=_SCREAMING_SNAKE_CASE				,		labelaid=_SCREAMING_SNAKE_CASE			)

      SCREAMING_SNAKE_CASE	=		{
          """regnet-x-002""": ImageNetPreTrainedConfig(
              depths=[1, 1, 4, 7]				,		hidden_sizes=[24, 56, 1_52, 3_68]				,		groups_width=8				,		layer_type="""x"""			),
          """regnet-x-004""": ImageNetPreTrainedConfig(
              depths=[1, 2, 7, 12]				,		hidden_sizes=[32, 64, 1_60, 3_84]				,		groups_width=16				,		layer_type="""x"""			),
          """regnet-x-006""": ImageNetPreTrainedConfig(
              depths=[1, 3, 5, 7]				,		hidden_sizes=[48, 96, 2_40, 5_28]				,		groups_width=24				,		layer_type="""x"""			),
          """regnet-x-008""": ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 5]				,		hidden_sizes=[64, 1_28, 2_88, 6_72]				,		groups_width=16				,		layer_type="""x"""			),
          """regnet-x-016""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 2]				,		hidden_sizes=[72, 1_68, 4_08, 9_12]				,		groups_width=24				,		layer_type="""x"""			),
          """regnet-x-032""": ImageNetPreTrainedConfig(
              depths=[2, 6, 15, 2]				,		hidden_sizes=[96, 1_92, 4_32, 10_08]				,		groups_width=48				,		layer_type="""x"""			),
          """regnet-x-040""": ImageNetPreTrainedConfig(
              depths=[2, 5, 14, 2]				,		hidden_sizes=[80, 2_40, 5_60, 13_60]				,		groups_width=40				,		layer_type="""x"""			),
          """regnet-x-064""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]				,		hidden_sizes=[1_68, 3_92, 7_84, 16_24]				,		groups_width=56				,		layer_type="""x"""			),
          """regnet-x-080""": ImageNetPreTrainedConfig(
              depths=[2, 5, 15, 1]				,		hidden_sizes=[80, 2_40, 7_20, 19_20]				,		groups_width=1_20				,		layer_type="""x"""			),
          """regnet-x-120""": ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]				,		hidden_sizes=[2_24, 4_48, 8_96, 22_40]				,		groups_width=1_12				,		layer_type="""x"""			),
          """regnet-x-160""": ImageNetPreTrainedConfig(
              depths=[2, 6, 13, 1]				,		hidden_sizes=[2_56, 5_12, 8_96, 20_48]				,		groups_width=1_28				,		layer_type="""x"""			),
          """regnet-x-320""": ImageNetPreTrainedConfig(
              depths=[2, 7, 13, 1]				,		hidden_sizes=[3_36, 6_72, 13_44, 25_20]				,		groups_width=1_68				,		layer_type="""x"""			),
          # y variant
          """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]				,		hidden_sizes=[24, 56, 1_52, 3_68]				,		groups_width=8			),
          """regnet-y-004""": ImageNetPreTrainedConfig(
              depths=[1, 3, 6, 6]				,		hidden_sizes=[48, 1_04, 2_08, 4_40]				,		groups_width=8			),
          """regnet-y-006""": ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 4]				,		hidden_sizes=[48, 1_12, 2_56, 6_08]				,		groups_width=16			),
          """regnet-y-008""": ImageNetPreTrainedConfig(
              depths=[1, 3, 8, 2]				,		hidden_sizes=[64, 1_28, 3_20, 7_68]				,		groups_width=16			),
          """regnet-y-016""": ImageNetPreTrainedConfig(
              depths=[2, 6, 17, 2]				,		hidden_sizes=[48, 1_20, 3_36, 8_88]				,		groups_width=24			),
          """regnet-y-032""": ImageNetPreTrainedConfig(
              depths=[2, 5, 13, 1]				,		hidden_sizes=[72, 2_16, 5_76, 15_12]				,		groups_width=24			),
          """regnet-y-040""": ImageNetPreTrainedConfig(
              depths=[2, 6, 12, 2]				,		hidden_sizes=[1_28, 1_92, 5_12, 10_88]				,		groups_width=64			),
          """regnet-y-064""": ImageNetPreTrainedConfig(
              depths=[2, 7, 14, 2]				,		hidden_sizes=[1_44, 2_88, 5_76, 12_96]				,		groups_width=72			),
          """regnet-y-080""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]				,		hidden_sizes=[1_68, 4_48, 8_96, 20_16]				,		groups_width=56			),
          """regnet-y-120""": ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]				,		hidden_sizes=[2_24, 4_48, 8_96, 22_40]				,		groups_width=1_12			),
          """regnet-y-160""": ImageNetPreTrainedConfig(
              depths=[2, 4, 11, 1]				,		hidden_sizes=[2_24, 4_48, 12_32, 30_24]				,		groups_width=1_12			),
          """regnet-y-320""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]				,		hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,		groups_width=2_32			),
          # models created by SEER -> https://arxiv.org/abs/2202.08360
          """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]				,		hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,		groups_width=2_32			),
          """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]				,		hidden_sizes=[3_28, 9_84, 19_68, 49_20]				,		groups_width=3_28			),
          """regnet-y-1280-seer""": RegNetConfig(
              depths=[2, 7, 17, 1]				,		hidden_sizes=[5_28, 10_56, 29_04, 73_92]				,		groups_width=2_64			),
          """regnet-y-2560-seer""": RegNetConfig(
              depths=[3, 7, 16, 1]				,		hidden_sizes=[6_40, 16_96, 25_44, 50_88]				,		groups_width=6_40			),
          """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]				,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,		groups_width=10_10			),
          # finetuned on imagenet
          """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]				,		hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,		groups_width=2_32			),
          """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]				,		hidden_sizes=[3_28, 9_84, 19_68, 49_20]				,		groups_width=3_28			),
          """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]				,		hidden_sizes=[5_28, 10_56, 29_04, 73_92]				,		groups_width=2_64			),
          """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[3, 7, 16, 1]				,		hidden_sizes=[6_40, 16_96, 25_44, 50_88]				,		groups_width=6_40			),
          """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]				,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,		groups_width=10_10			),
      }

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

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

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

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

      SCREAMING_SNAKE_CASE	=		partial(
          _SCREAMING_SNAKE_CASE				,		"""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	=		partial(
          _SCREAMING_SNAKE_CASE				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""				,		lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27				,		group_width=10_10				,		w_a=17_44				,		w_a=620.83				,		w_m=2.52			)			)			)				,		)

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

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

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

      SCREAMING_SNAKE_CASE	=		partial(
          _SCREAMING_SNAKE_CASE				,		"""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=10_10				,		w_a=17_44				,		w_a=620.83				,		w_m=2.52			)			)			)				,		)

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









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      _validate_point(_SCREAMING_SNAKE_CASE			)
      _validate_point(_SCREAMING_SNAKE_CASE			)
      if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
            raise ValueError("""Both points must be in the same n-dimensional space"""			)

      return float(sum(abs(a - b			) for a, b in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)			)			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     None:


      '''simple docstring'''


      if point:
            if isinstance(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			):
                  for item in point:
                        if not isinstance(_SCREAMING_SNAKE_CASE				,		(int, float)			):
                              SCREAMING_SNAKE_CASE	=		(
                                  """Expected a list of numbers as input, found """
                                  F"""{type(_SCREAMING_SNAKE_CASE			).__name__}"""
                              )
                              raise TypeError(_SCREAMING_SNAKE_CASE			)
            else:
                  SCREAMING_SNAKE_CASE	=		F"""Expected a list of numbers as input, found {type(_SCREAMING_SNAKE_CASE			).__name__}"""
                  raise TypeError(_SCREAMING_SNAKE_CASE			)
      else:
            raise ValueError("""Missing an input"""			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      _validate_point(_SCREAMING_SNAKE_CASE			)
      _validate_point(_SCREAMING_SNAKE_CASE			)
      if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
            raise ValueError("""Both points must be in the same n-dimensional space"""			)

      return float(sum(abs(x - y			) for x, y in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)			)			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()


