






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

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

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


logging.set_verbosity_info()
a_					=	logging.get_logger()






@dataclass
class        __SCREAMING_SNAKE_CASE :
 snake_case_												=							42
 snake_case_												=							field(default_factory=lowerCamelCase__			)
 snake_case_												=							field(default_factory=lowerCamelCase__			)




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							str   , __lowercase		:							Optional[Any]   , __lowercase		:							Dict )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Any													=len(list(m.modules() ) ) == 1 or isinstance(snake_case__   , nn.Convad ) or isinstance(snake_case__   , nn.BatchNormad )
       if has_not_submodules:
             self.traced.append(snake_case__ )




 def __call__( self		:							Tuple   , __lowercase		:							Union[str, Any] )		->  Dict:





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




 @property
 def        __magic_name__		( self		:							List[str] )		->  Tuple:
       return list(filter(lambda __lowercase : len(list(x.state_dict().keys() ) ) > 0   , self.traced ) )






@dataclass
class        __SCREAMING_SNAKE_CASE :
 snake_case_												=							42
 snake_case_												=							42
 snake_case_												=							1
 snake_case_												=							field(default_factory=lowerCamelCase__			)
 snake_case_												=							field(default_factory=lowerCamelCase__			)
 snake_case_												=							True




 def __call__( self		:							Dict   , __lowercase		:							Tuple )		->  int:
       SCREAMING_SNAKE_CASE__       :			Tuple													=Tracker(self.dest )(snake_case__ ).parametrized
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=Tracker(self.src )(snake_case__ ).parametrized

       SCREAMING_SNAKE_CASE__       :			List[Any]													=list(filter(lambda __lowercase : type(snake_case__ ) not in self.src_skip   , snake_case__ ) )
       SCREAMING_SNAKE_CASE__       :			List[str]													=list(filter(lambda __lowercase : type(snake_case__ ) not in self.dest_skip   , snake_case__ ) )

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

       for dest_m, src_m in zip(snake_case__   , snake_case__ ):
             dest_m.load_state_dict(src_m.state_dict() )
             if self.verbose == 1:
                   print(F"Transfered from={src_m} to={dest_m}" )






class        __SCREAMING_SNAKE_CASE (     nn.Module			):




 def __init__( self		:							Optional[int]   , __lowercase		:							int )		->  Tuple:
       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__       :			Tuple													=len(snake_case__ ) + 1
             feature_blocks.append((F"res{block_index}", v) )

       SCREAMING_SNAKE_CASE__       :			int													=nn.ModuleDict(snake_case__ )




 def        __magic_name__		( self		:							int   , __lowercase		:							List[str] )		->  Optional[int]:
       return get_trunk_forward_outputs(
           snake_case__   , out_feat_keys=snake_case__   , feature_blocks=self._feature_blocks   , )






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase__			):




 def        __magic_name__		( self		:							Tuple   , __lowercase		:							Any )		->  str:
       SCREAMING_SNAKE_CASE__       :			str													=x.split('''-''' )
       return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )




 def __getitem__( self		:							List[Any]   , __lowercase		:							Union[str, Any] )		->  Callable[[], Tuple[nn.Module, Dict]]:





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

       else:
             SCREAMING_SNAKE_CASE__       :			Any													=super().__getitem__(snake_case__ )

       return val






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase__			):




 def __getitem__( self		:							Optional[Any]   , __lowercase		:							int )		->  Callable[[], nn.Module]:





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





def 	_a(					UpperCamelCase__			:  Any,		UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  List[str]    ):




      '''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  Any,		UpperCamelCase__			:  Any,		UpperCamelCase__			:  str = True,		):




      '''simple docstring'''






      print(f"Converting {name}..."    )
      with torch.no_grad():
            SCREAMING_SNAKE_CASE__       :			List[str]													=from_model_func()
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=our_model_func(__lowerCAmelCase    ).eval()
            SCREAMING_SNAKE_CASE__       :			List[str]													=ModuleTransfer(src=__lowerCAmelCase,		dest=__lowerCAmelCase,		raise_if_mismatch=__lowerCAmelCase    )
            SCREAMING_SNAKE_CASE__       :			Dict													=torch.randn((1, 3, 2_2_4, 2_2_4)    )
            module_transfer(__lowerCAmelCase    )

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

      SCREAMING_SNAKE_CASE__       :			List[str]													=our_model(__lowerCAmelCase,		output_hidden_states=__lowerCAmelCase    )
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=(
          our_outputs.logits if isinstance(__lowerCAmelCase,		__lowerCAmelCase    ) else our_outputs.last_hidden_state
      )

      SCREAMING_SNAKE_CASE__       :			Any													=from_model(__lowerCAmelCase    )
      SCREAMING_SNAKE_CASE__       :			List[Any]													=from_output[-1] if type(__lowerCAmelCase    ) 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__       :			int													=our_outputs.hidden_states[-1]

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

            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=2_2_4 if '''seer''' not in name else 3_8_4
            # we can use the convnext one
            SCREAMING_SNAKE_CASE__       :			Dict													=AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''',		size=__lowerCAmelCase    )
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / name,		commit_message='''Add image processor''',		use_temp_dir=__lowerCAmelCase,		)

            print(f"Pushed {name}"    )





def 	_a(					UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  Optional[int] = None,		UpperCamelCase__			:  Optional[int] = True    ):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			str													='''imagenet-1k-id2label.json'''
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=1_0_0_0
      SCREAMING_SNAKE_CASE__       :			int													=(1, num_labels)

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

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

      SCREAMING_SNAKE_CASE__       :			Any													=partial(__lowerCAmelCase,		num_labels=__lowerCAmelCase,		idalabel=__lowerCAmelCase,		labelaid=__lowerCAmelCase    )

      SCREAMING_SNAKE_CASE__       :			int													={
          '''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    ),
      }

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=NameToOurModelFuncMap()
      SCREAMING_SNAKE_CASE__       :			Any													=NameToFromModelFuncMap()
      # add seer weights logic

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

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

      SCREAMING_SNAKE_CASE__       :			List[str]													=partial(
          __lowerCAmelCase,		'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch''',		lambda: FakeRegNetVisslWrapper(RegNetYaagf()    ),		)

      SCREAMING_SNAKE_CASE__       :			Optional[int]													=partial(
          __lowerCAmelCase,		'''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__       :			Optional[int]													=partial(
          __lowerCAmelCase,		'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch''',		lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=2_7,		group_width=1_0_1_0,		w_a=1_7_4_4,		w_a=6_2_0.8_3,		w_m=2.5_2    )    )    ),		)

      # IN1K finetuned
      SCREAMING_SNAKE_CASE__       :			Dict													=partial(
          __lowerCAmelCase,		'''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(
          __lowerCAmelCase,		'''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[Any]													=partial(
          __lowerCAmelCase,		'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch''',		lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    ),		)

      SCREAMING_SNAKE_CASE__       :			Optional[int]													=partial(
          __lowerCAmelCase,		'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch''',		lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=2_7,		group_width=1_0_1_0,		w_a=1_7_4_4,		w_a=6_2_0.8_3,		w_m=2.5_2    )    )    ),		)

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


if __name__ == "__main__":
  a_					=	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.',
  )

  a_					=	parser.parse_args()

  a_					=	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)







'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					=	logging.get_logger(__name__)

a_					=	{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							"""biogpt"""




 def __init__( self		:							str   , __lowercase		:							Union[str, Any]=4_23_84   , __lowercase		:							Union[str, Any]=10_24   , __lowercase		:							Any=24   , __lowercase		:							Any=16   , __lowercase		:							Optional[Any]=40_96   , __lowercase		:							Any="gelu"   , __lowercase		:							Optional[Any]=0.1   , __lowercase		:							List[Any]=0.1   , __lowercase		:							Union[str, Any]=10_24   , __lowercase		:							List[Any]=0.02   , __lowercase		:							Tuple=1e-12   , __lowercase		:							Optional[Any]=True   , __lowercase		:							Optional[Any]=True   , __lowercase		:							Any=0.0   , __lowercase		:							int=0.0   , __lowercase		:							str=1   , __lowercase		:							int=0   , __lowercase		:							Optional[int]=2   , **__lowercase		:							Dict   , )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=vocab_size
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=max_position_embeddings
       SCREAMING_SNAKE_CASE__       :			str													=hidden_size
       SCREAMING_SNAKE_CASE__       :			int													=num_hidden_layers
       SCREAMING_SNAKE_CASE__       :			Tuple													=num_attention_heads
       SCREAMING_SNAKE_CASE__       :			Any													=intermediate_size
       SCREAMING_SNAKE_CASE__       :			int													=hidden_act
       SCREAMING_SNAKE_CASE__       :			List[Any]													=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Dict													=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=initializer_range
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=layer_norm_eps
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=scale_embedding
       SCREAMING_SNAKE_CASE__       :			str													=use_cache
       SCREAMING_SNAKE_CASE__       :			str													=layerdrop
       SCREAMING_SNAKE_CASE__       :			Dict													=activation_dropout
       super().__init__(pad_token_id=__lowercase   , bos_token_id=__lowercase   , eos_token_id=__lowercase   , **__lowercase )