
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase							=       {
    """configuration_blenderbot_small""": [
        """BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlenderbotSmallConfig""",
        """BlenderbotSmallOnnxConfig""",
    ],
    """tokenization_blenderbot_small""": ["""BlenderbotSmallTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""BlenderbotSmallTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BlenderbotSmallForCausalLM""",
       """BlenderbotSmallForConditionalGeneration""",
       """BlenderbotSmallModel""",
       """BlenderbotSmallPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """TFBlenderbotSmallForConditionalGeneration""",
       """TFBlenderbotSmallModel""",
       """TFBlenderbotSmallPreTrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """FlaxBlenderbotSmallForConditionalGeneration""",
       """FlaxBlenderbotSmallModel""",
       """FlaxBlenderbotSmallPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_blenderbot_small import (
       BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BlenderbotSmallConfig,
       BlenderbotSmallOnnxConfig,
   )
   from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_blenderbot_small import (
          BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
          BlenderbotSmallForCausalLM,
          BlenderbotSmallForConditionalGeneration,
          BlenderbotSmallModel,
          BlenderbotSmallPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_blenderbot_small import (
          TFBlenderbotSmallForConditionalGeneration,
          TFBlenderbotSmallModel,
          TFBlenderbotSmallPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_blenderbot_small import (
          FlaxBlenderbotSmallForConditionalGeneration,
          FlaxBlenderbotSmallModel,
          FlaxBlenderbotSmallPreTrainedModel,
      )

else:
   import sys

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







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   UpperCAmelCase		:
   A__					:		nn.Module
   A__					:		List[nn.Module]						     =     field(default_factory=A_ )
   A__					:		list						     =     field(default_factory=A_ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Tensor    ,						snake_case__		:					Tensor )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   List[str]							=      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		:					List[Any]    ,						snake_case__		:					Tensor )		->	List[Any]:


          '''simple docstring'''


          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 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Optional[int]:


          '''simple docstring'''


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






@dataclass
class   UpperCAmelCase		:
   A__					:		nn.Module
   A__					:		nn.Module
   A__					:		int						     =     1
   A__					:		List						     =     field(default_factory=A_ )
   A__					:		List						     =     field(default_factory=A_ )
   A__					:		bool						     =     True



   def __call__(self		:					List[Any]    ,						snake_case__		:					Tensor )		->	Any:


          '''simple docstring'''


          snake_case       :   str							=      Tracker(self.dest )(snake_case__ ).parametrized
          snake_case       :   Optional[int]							=      Tracker(self.src )(snake_case__ ).parametrized

          snake_case       :   List[str]							=      list(filter(lambda snake_case__ : type(snake_case__ ) not in self.src_skip    ,						snake_case__ ) )
          snake_case       :   Optional[Any]							=      list(filter(lambda snake_case__ : 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   UpperCAmelCase		(						nn.Module ):



   def __init__(self		:					Tuple    ,						snake_case__		:					nn.Module )		->	Optional[Any]:


          '''simple docstring'''


          super().__init__()

          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}"""
                 snake_case       :   Union[str, Any]							=      len(snake_case__ ) + 1
                 feature_blocks.append((f"""res{block_index}""", v) )

          snake_case       :   Optional[Any]							=      nn.ModuleDict(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					Tensor )		->	Dict:


          '''simple docstring'''


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






class   UpperCAmelCase		(						A_ ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					str )		->	str:


          '''simple docstring'''


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



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


          '''simple docstring'''


          if x not in self:
                 snake_case       :   Dict							=      self.convert_name_to_timm(snake_case__ )
                 snake_case       :   Union[str, Any]							=      partial(lambda: (timm.create_model(snake_case__    ,						pretrained=snake_case__ ).eval(), None) )

          else:
                 snake_case       :   List[str]							=      super().__getitem__(snake_case__ )

          return val






class   UpperCAmelCase		(						A_ ):



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


          '''simple docstring'''


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



def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : List[Tuple[str, str]]    ):
       for from_key, to_key in keys:
              snake_case       :   str							=      from_state_dict[from_key].clone()
              print(f"""Copied key={from_key} to={to_key}"""    )
       return to_state_dict


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : Callable[[], nn.Module]				,					__lowerCamelCase     : Callable[[], nn.Module]				,					__lowerCamelCase     : RegNetConfig				,					__lowerCamelCase     : Path				,					__lowerCamelCase     : bool = True				,					):
       print(f"""Converting {name}..."""    )
       with torch.no_grad():
              snake_case				,					snake_case       :   int							=      from_model_func()
              snake_case       :   str							=      our_model_func(__lowerCamelCase    ).eval()
              snake_case       :   int							=      ModuleTransfer(src=__lowerCamelCase				,					dest=__lowerCamelCase				,					raise_if_mismatch=__lowerCamelCase    )
              snake_case       :   Dict							=      torch.randn((1, 3, 224, 224)    )
              module_transfer(__lowerCamelCase    )

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

       snake_case       :   Any							=      our_model(__lowerCamelCase				,					output_hidden_states=__lowerCamelCase    )
       snake_case       :   Union[str, Any]							=      (
           our_outputs.logits if isinstance(__lowerCamelCase				,					__lowerCamelCase    ) else our_outputs.last_hidden_state
       )

       snake_case       :   Union[str, Any]							=      from_model(__lowerCamelCase    )
       snake_case       :   Dict							=      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:
              snake_case       :   Any							=      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				,					)

              snake_case       :   List[str]							=      224 if "seer" not in name else 384
              # we can use the convnext one
              snake_case       :   int							=      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 			UpperCamelCase					(    __lowerCamelCase     : Path				,					__lowerCamelCase     : str = None				,					__lowerCamelCase     : bool = True    ):
       snake_case       :   Union[str, Any]							=      "imagenet-1k-id2label.json"
       snake_case       :   List[str]							=      1000
       snake_case       :   List[str]							=      (1, num_labels)

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

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

       snake_case       :   Dict							=      partial(__lowerCamelCase				,					num_labels=__lowerCamelCase				,					idalabel=__lowerCamelCase				,					labelaid=__lowerCamelCase    )

       snake_case       :   Optional[Any]							=      {
           "regnet-x-002": ImageNetPreTrainedConfig(
               depths=[1, 1, 4, 7]				,					hidden_sizes=[24, 56, 152, 368]				,					groups_width=8				,					layer_type="x"    ),
           "regnet-x-004": ImageNetPreTrainedConfig(
               depths=[1, 2, 7, 12]				,					hidden_sizes=[32, 64, 160, 384]				,					groups_width=16				,					layer_type="x"    ),
           "regnet-x-006": ImageNetPreTrainedConfig(
               depths=[1, 3, 5, 7]				,					hidden_sizes=[48, 96, 240, 528]				,					groups_width=24				,					layer_type="x"    ),
           "regnet-x-008": ImageNetPreTrainedConfig(
               depths=[1, 3, 7, 5]				,					hidden_sizes=[64, 128, 288, 672]				,					groups_width=16				,					layer_type="x"    ),
           "regnet-x-016": ImageNetPreTrainedConfig(
               depths=[2, 4, 10, 2]				,					hidden_sizes=[72, 168, 408, 912]				,					groups_width=24				,					layer_type="x"    ),
           "regnet-x-032": ImageNetPreTrainedConfig(
               depths=[2, 6, 15, 2]				,					hidden_sizes=[96, 192, 432, 1008]				,					groups_width=48				,					layer_type="x"    ),
           "regnet-x-040": ImageNetPreTrainedConfig(
               depths=[2, 5, 14, 2]				,					hidden_sizes=[80, 240, 560, 1360]				,					groups_width=40				,					layer_type="x"    ),
           "regnet-x-064": ImageNetPreTrainedConfig(
               depths=[2, 4, 10, 1]				,					hidden_sizes=[168, 392, 784, 1624]				,					groups_width=56				,					layer_type="x"    ),
           "regnet-x-080": ImageNetPreTrainedConfig(
               depths=[2, 5, 15, 1]				,					hidden_sizes=[80, 240, 720, 1920]				,					groups_width=120				,					layer_type="x"    ),
           "regnet-x-120": ImageNetPreTrainedConfig(
               depths=[2, 5, 11, 1]				,					hidden_sizes=[224, 448, 896, 2240]				,					groups_width=112				,					layer_type="x"    ),
           "regnet-x-160": ImageNetPreTrainedConfig(
               depths=[2, 6, 13, 1]				,					hidden_sizes=[256, 512, 896, 2048]				,					groups_width=128				,					layer_type="x"    ),
           "regnet-x-320": ImageNetPreTrainedConfig(
               depths=[2, 7, 13, 1]				,					hidden_sizes=[336, 672, 1344, 2520]				,					groups_width=168				,					layer_type="x"    ),
           # y variant
           "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]				,					hidden_sizes=[24, 56, 152, 368]				,					groups_width=8    ),
           "regnet-y-004": ImageNetPreTrainedConfig(
               depths=[1, 3, 6, 6]				,					hidden_sizes=[48, 104, 208, 440]				,					groups_width=8    ),
           "regnet-y-006": ImageNetPreTrainedConfig(
               depths=[1, 3, 7, 4]				,					hidden_sizes=[48, 112, 256, 608]				,					groups_width=16    ),
           "regnet-y-008": ImageNetPreTrainedConfig(
               depths=[1, 3, 8, 2]				,					hidden_sizes=[64, 128, 320, 768]				,					groups_width=16    ),
           "regnet-y-016": ImageNetPreTrainedConfig(
               depths=[2, 6, 17, 2]				,					hidden_sizes=[48, 120, 336, 888]				,					groups_width=24    ),
           "regnet-y-032": ImageNetPreTrainedConfig(
               depths=[2, 5, 13, 1]				,					hidden_sizes=[72, 216, 576, 1512]				,					groups_width=24    ),
           "regnet-y-040": ImageNetPreTrainedConfig(
               depths=[2, 6, 12, 2]				,					hidden_sizes=[128, 192, 512, 1088]				,					groups_width=64    ),
           "regnet-y-064": ImageNetPreTrainedConfig(
               depths=[2, 7, 14, 2]				,					hidden_sizes=[144, 288, 576, 1296]				,					groups_width=72    ),
           "regnet-y-080": ImageNetPreTrainedConfig(
               depths=[2, 4, 10, 1]				,					hidden_sizes=[168, 448, 896, 2016]				,					groups_width=56    ),
           "regnet-y-120": ImageNetPreTrainedConfig(
               depths=[2, 5, 11, 1]				,					hidden_sizes=[224, 448, 896, 2240]				,					groups_width=112    ),
           "regnet-y-160": ImageNetPreTrainedConfig(
               depths=[2, 4, 11, 1]				,					hidden_sizes=[224, 448, 1232, 3024]				,					groups_width=112    ),
           "regnet-y-320": ImageNetPreTrainedConfig(
               depths=[2, 5, 12, 1]				,					hidden_sizes=[232, 696, 1392, 3712]				,					groups_width=232    ),
           # models created by SEER -> https://arxiv.org/abs/2202.08360
           "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]				,					hidden_sizes=[232, 696, 1392, 3712]				,					groups_width=232    ),
           "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]				,					hidden_sizes=[328, 984, 1968, 4920]				,					groups_width=328    ),
           "regnet-y-1280-seer": RegNetConfig(
               depths=[2, 7, 17, 1]				,					hidden_sizes=[528, 1056, 2904, 7392]				,					groups_width=264    ),
           "regnet-y-2560-seer": RegNetConfig(
               depths=[3, 7, 16, 1]				,					hidden_sizes=[640, 1696, 2544, 5088]				,					groups_width=640    ),
           "regnet-y-10b-seer": ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]				,					hidden_sizes=[2020, 4040, 11110, 28280]				,					groups_width=1010    ),
           # finetuned on imagenet
           "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
               depths=[2, 5, 12, 1]				,					hidden_sizes=[232, 696, 1392, 3712]				,					groups_width=232    ),
           "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
               depths=[2, 5, 12, 1]				,					hidden_sizes=[328, 984, 1968, 4920]				,					groups_width=328    ),
           "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]				,					hidden_sizes=[528, 1056, 2904, 7392]				,					groups_width=264    ),
           "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
               depths=[3, 7, 16, 1]				,					hidden_sizes=[640, 1696, 2544, 5088]				,					groups_width=640    ),
           "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]				,					hidden_sizes=[2020, 4040, 11110, 28280]				,					groups_width=1010    ),
       }

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

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

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

       snake_case       :   Optional[int]							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,					)

       snake_case       :   List[str]							=      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()    )				,					)

       snake_case       :   Tuple							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"				,					lambda: FakeRegNetVisslWrapper(
               RegNet(RegNetParams(depth=27				,					group_width=1010				,					w_a=1744				,					w_a=620.83				,					w_m=2.52    )    )    )				,					)

       # IN1K finetuned
       snake_case       :   List[Any]							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,					)

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

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

       snake_case       :   Dict							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"				,					lambda: FakeRegNetVisslWrapper(
               RegNet(RegNetParams(depth=27				,					group_width=1010				,					w_a=1744				,					w_a=620.83				,					w_m=2.52    )    )    )				,					)

       if model_name:
              convert_weight_and_push(
                  __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__":
   __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)





