


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase         =			logging.get_logger()


@dataclass
class   snake_case_ :




       """simple docstring"""






       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				field(default_factory=lowercase__  )
       _lowerCamelCase			    =				field(default_factory=lowercase__  )



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		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     ,lowercase):

             """simple docstring"""





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



       @property
       def  A_   (     self):

             """simple docstring"""





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


@dataclass
class   snake_case_ :




       """simple docstring"""






       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				1
       _lowerCamelCase			    =				field(default_factory=lowercase__  )
       _lowerCamelCase			    =				field(default_factory=lowercase__  )
       _lowerCamelCase			    =				True



       def __call__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		Tracker(self.dest)(UpperCAmelCase__).parametrized
             UpperCAmelCase_  :	Union[str, Any]				      =		Tracker(self.src)(UpperCAmelCase__).parametrized

             UpperCAmelCase_  :	Optional[int]				      =		list(filter(lambda lowercase: type(UpperCAmelCase__) not in self.src_skip     ,UpperCAmelCase__))
             UpperCAmelCase_  :	int				      =		list(filter(lambda lowercase: 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   snake_case_ (nn.Module  ):




       """simple docstring"""






       def __init__(     self     ,lowercase):

             """simple docstring"""





             super().__init__()

             UpperCAmelCase_  :	Union[str, Any]				      =		[]
             # - 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_  :	List[str]				      =		len(UpperCAmelCase__) + 1
                   feature_blocks.append((F"""res{block_index}""", v))

             UpperCAmelCase_  :	str				      =		nn.ModuleDict(UpperCAmelCase__)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





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


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       def  A_   (     self     ,lowercase):

             """simple docstring"""





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



       def __getitem__(     self     ,lowercase):

             """simple docstring"""





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

             else:
                   UpperCAmelCase_  :	str				      =		super().__getitem__(UpperCAmelCase__)

             return val


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       def __getitem__(     self     ,lowercase):

             """simple docstring"""





             if "seer" in x and "in1k" not in x:
                   UpperCAmelCase_  :	Dict				      =		RegNetModel
             else:
                   UpperCAmelCase_  :	Optional[Any]				      =		RegNetForImageClassification
             return val







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







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







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case = True					,  )     ->     int:






      '''simple docstring'''







      print(F"""Converting {name}..."""    )
      with torch.no_grad():
            UpperCAmelCase_      ,      UpperCAmelCase_  :	List[Any]				      =		from_model_func()
            UpperCAmelCase_  :	Optional[Any]				      =		our_model_func(_UpperCamelCase    ).eval()
            UpperCAmelCase_  :	Union[str, 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_  :	Dict				      =		[]
            # for seer - in1k finetuned we have to manually copy the head
            if "seer" in name and "in1k" in name:
                  UpperCAmelCase_  :	str				      =		[("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
            UpperCAmelCase_  :	Optional[int]				      =		manually_copy_vissl_head(_UpperCamelCase					,  our_model.state_dict()					,  _UpperCamelCase    )
            our_model.load_state_dict(_UpperCamelCase    )

      UpperCAmelCase_  :	Any				      =		our_model(_UpperCamelCase					,  output_hidden_states=_UpperCamelCase    )
      UpperCAmelCase_  :	Any				      =		(
          our_outputs.logits if isinstance(_UpperCamelCase					,  _UpperCamelCase    ) else our_outputs.last_hidden_state
      )

      UpperCAmelCase_  :	List[str]				      =		from_model(_UpperCamelCase    )
      UpperCAmelCase_  :	List[Any]				      =		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_  :	int				      =		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_  :	Any				      =		2_2_4 if "seer" not in name else 3_8_4
            # we can use the convnext one
            UpperCAmelCase_  :	Optional[int]				      =		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    _snake_case (       __snake_case					,  __snake_case = None					,  __snake_case = True    )     ->     Any:






      '''simple docstring'''







      UpperCAmelCase_  :	str				      =		"imagenet-1k-id2label.json"
      UpperCAmelCase_  :	Union[str, Any]				      =		1_0_0_0
      UpperCAmelCase_  :	Dict				      =		(1, num_labels)

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

      UpperCAmelCase_  :	Union[str, Any]				      =		idalabel
      UpperCAmelCase_  :	str				      =		{v: k for k, v in idalabel.items()}

      UpperCAmelCase_  :	int				      =		partial(_UpperCamelCase					,  num_labels=_UpperCamelCase					,  idalabel=_UpperCamelCase					,  labelaid=_UpperCamelCase    )

      UpperCAmelCase_  :	List[str]				      =		{
          "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_  :	Optional[Any]				      =		NameToOurModelFuncMap()
      UpperCAmelCase_  :	List[Any]				      =		NameToFromModelFuncMap()
      # add seer weights logic

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

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

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

      UpperCAmelCase_  :	List[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_  :	Optional[int]				      =		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=620.83					,  w_m=2.52    )    )    )					,  )

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

      UpperCAmelCase_  :	Optional[int]				      =		partial(
          _UpperCamelCase					,  "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"					,  lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )					,  )

      UpperCAmelCase_  :	List[str]				      =		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=620.83					,  w_m=2.52    )    )    )					,  )

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


if __name__ == "__main__":
   __lowerCamelCase         =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--model_name''',
       default=None,
       type=str,
       help=(
           '''The name of the model you wish to convert, it must be one of the supported regnet* architecture,'''
           ''' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''',
       default=None,
       type=Path,
       required=True,
       help='''Path to the output PyTorch model directory.''',
   )
   parser.add_argument(
       '''--push_to_hub''',
       default=True,
       type=bool,
       required=False,
       help='''If True, push model and image processor to the hub.''',
   )

   __lowerCamelCase         =			parser.parse_args()

   __lowerCamelCase         =			args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		FunnelConfig.from_json_file(__snake_case    )
      print(F"""Building PyTorch model from configuration: {config}"""    )
      UpperCAmelCase_  :	str				      =		FunnelBaseModel(__snake_case    ) if base_model else FunnelModel(__snake_case    )

      # Load weights from tf checkpoint
      load_tf_weights_in_funnel(__snake_case					,  __snake_case					,  __snake_case    )

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""    )
      torch.save(model.state_dict()					,  __snake_case    )


if __name__ == "__main__":
   __lowerCamelCase         =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
   )
   __lowerCamelCase         =			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
   )
