



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_									=							logging.get_logger()





@dataclass
class 		UpperCAmelCase						:
   SCREAMING_SNAKE_CASE__							=     4_2
   SCREAMING_SNAKE_CASE__							=     field(default_factory=snake_case_				)
   SCREAMING_SNAKE_CASE__							=     field(default_factory=snake_case_				)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  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       ,     _lowerCAmelCase			):
         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 	__lowerCAmelCase							(	self			):
         # check the len of the state_dict keys to see if we have learnable params
         return list(filter(lambda _lowerCAmelCase			: len(list(x.state_dict().keys()			)			) > 0       ,     self.traced			)			)






@dataclass
class 		UpperCAmelCase						:
   SCREAMING_SNAKE_CASE__							=     4_2
   SCREAMING_SNAKE_CASE__							=     4_2
   SCREAMING_SNAKE_CASE__							=     1
   SCREAMING_SNAKE_CASE__							=     field(default_factory=snake_case_				)
   SCREAMING_SNAKE_CASE__							=     field(default_factory=snake_case_				)
   SCREAMING_SNAKE_CASE__							=     True





   def __call__(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  Tracker(self.dest			)(_lowerCAmelCase			).parametrized
         _lowerCAmelCase 			=  Tracker(self.src			)(_lowerCAmelCase			).parametrized

         _lowerCAmelCase 			=  list(filter(lambda _lowerCAmelCase			: type(_lowerCAmelCase			) not in self.src_skip       ,     _lowerCAmelCase			)			)
         _lowerCAmelCase 			=  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				):





   def __init__(	self       ,     _lowerCAmelCase			):
         super().__init__()

         _lowerCAmelCase 			=  []
         # - 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}'''
               _lowerCAmelCase 			=  len(_lowerCAmelCase			) + 1
               feature_blocks.append((F'''res{block_index}''', v)			)

         _lowerCAmelCase 			=  nn.ModuleDict(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         return get_trunk_forward_outputs(
             _lowerCAmelCase       ,     out_feat_keys=_lowerCAmelCase       ,     feature_blocks=self._feature_blocks       ,     )






class 		UpperCAmelCase						(						snake_case_				):





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  x.split('''-'''			)
         return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]			)





   def __getitem__(	self       ,     _lowerCAmelCase			):
         # default to timm!
         if x not in self:
               _lowerCAmelCase 			=  self.convert_name_to_timm(_lowerCAmelCase			)
               _lowerCAmelCase 			=  partial(lambda: (timm.create_model(_lowerCAmelCase       ,     pretrained=_lowerCAmelCase			).eval(), None)			)

         else:
               _lowerCAmelCase 			=  super().__getitem__(_lowerCAmelCase			)

         return val






class 		UpperCAmelCase						(						snake_case_				):





   def __getitem__(	self       ,     _lowerCAmelCase			):
         if "seer" in x and "in1k" not in x:
               _lowerCAmelCase 			=  RegNetModel
         else:
               _lowerCAmelCase 			=  RegNetForImageClassification
         return val






def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Tuple[str, str]]				)->str:
      for from_key, to_key in keys:
            _lowerCAmelCase 			=  from_state_dict[from_key].clone()
            print(f'''Copied key={from_key} to={to_key}'''				)
      return to_state_dict
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	Callable[[], nn.Module]     ,				_SCREAMING_SNAKE_CASE   :	Callable[[], nn.Module]     ,				_SCREAMING_SNAKE_CASE   :	RegNetConfig     ,				_SCREAMING_SNAKE_CASE   :	Path     ,				_SCREAMING_SNAKE_CASE   :	bool = True     ,				)->Optional[Any]:
      print(f'''Converting {name}...'''				)
      with torch.no_grad():
            _lowerCAmelCase			,   _lowerCAmelCase 			=  from_model_func()
            _lowerCAmelCase 			=  our_model_func(_SCREAMING_SNAKE_CASE				).eval()
            _lowerCAmelCase 			=  ModuleTransfer(src=_SCREAMING_SNAKE_CASE     ,				dest=_SCREAMING_SNAKE_CASE     ,				raise_if_mismatch=_SCREAMING_SNAKE_CASE				)
            _lowerCAmelCase 			=  torch.randn((1, 3, 2_2_4, 2_2_4)				)
            module_transfer(_SCREAMING_SNAKE_CASE				)

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

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

      _lowerCAmelCase 			=  from_model(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  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:
            _lowerCAmelCase 			=  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     ,				)

            _lowerCAmelCase 			=  2_2_4 if '''seer''' not in name else 3_8_4
            # we can use the convnext one
            _lowerCAmelCase 			=  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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Path     ,				_SCREAMING_SNAKE_CASE   :	str = None     ,				_SCREAMING_SNAKE_CASE   :	bool = True				)->Any:
      _lowerCAmelCase 			=  '''imagenet-1k-id2label.json'''
      _lowerCAmelCase 			=  1_0_0_0
      _lowerCAmelCase 			=  (1, num_labels)

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

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

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

      _lowerCAmelCase 			=  {
          '''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				),
      }

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

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

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

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

      _lowerCAmelCase 			=  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()				)     ,				)

      _lowerCAmelCase 			=  partial(
          _SCREAMING_SNAKE_CASE     ,				'''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=620.83     ,				w_m=2.52				)				)				)     ,				)

      # IN1K finetuned
      _lowerCAmelCase 			=  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()				)     ,				)

      _lowerCAmelCase 			=  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()				)     ,				)

      _lowerCAmelCase 			=  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()				)     ,				)

      _lowerCAmelCase 			=  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=2_7     ,				group_width=1_0_1_0     ,				w_a=1_7_4_4     ,				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__":
    UpperCAmelCase_									=							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_									=							parser.parse_args()

    UpperCAmelCase_									=							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)




# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_									=							{
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CpmAntForCausalLM",
        "CpmAntModel",
        "CpmAntPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
    from .tokenization_cpmant import CpmAntTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cpmant import (
            CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CpmAntForCausalLM,
            CpmAntModel,
            CpmAntPreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase_									=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)