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 			UpperCAmelCase__    :



  """simple docstring"""






  lowerCAmelCase__		:	nn.Module
  lowerCAmelCase__		:	List[nn.Module]    		= field(default_factory=snake_case					)
  lowerCAmelCase__		:	list    		= field(default_factory=snake_case					)






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			Tensor				,				__lowerCAmelCase:			Tensor   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       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:			Dict				,				__lowerCAmelCase:			Tensor   ) -> Any:




    '''simple docstring'''




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






  @property
  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> int:




    '''simple docstring'''




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






@dataclass
class 			UpperCAmelCase__    :



  """simple docstring"""






  lowerCAmelCase__		:	nn.Module
  lowerCAmelCase__		:	nn.Module
  lowerCAmelCase__		:	int    		= 1
  lowerCAmelCase__		:	List    		= field(default_factory=snake_case					)
  lowerCAmelCase__		:	List    		= field(default_factory=snake_case					)
  lowerCAmelCase__		:	bool    		= True






  def __call__(				self:			Union[str, Any]				,				__lowerCAmelCase:			Tensor   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       Tracker(self.dest   )(__lowerCAmelCase   ).parametrized
    __UpperCAmelCase						    =       Tracker(self.src   )(__lowerCAmelCase   ).parametrized

    __UpperCAmelCase						    =       list(filter(lambda __lowerCAmelCase   : type(__lowerCAmelCase   ) not in self.src_skip				,				__lowerCAmelCase   )   )
    __UpperCAmelCase						    =       list(filter(lambda __lowerCAmelCase   : type(__lowerCAmelCase   ) not in self.dest_skip				,				__lowerCAmelCase   )   )

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

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






class 			UpperCAmelCase__    (		nn.Module					):



  """simple docstring"""






  def __init__(				self:			List[str]				,				__lowerCAmelCase:			nn.Module   ) -> Any:




    '''simple docstring'''




    super().__init__()

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

    __UpperCAmelCase						    =       nn.ModuleDict(__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Optional[Any]				,				__lowerCAmelCase:			Tensor   ) -> List[Any]:




    '''simple docstring'''




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






class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			str   ) -> str:




    '''simple docstring'''




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






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




    '''simple docstring'''




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

    else:
      __UpperCAmelCase						    =       super().__getitem__(__lowerCAmelCase   )

    return val






class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






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




    '''simple docstring'''




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






def       __lowerCAmelCase      (						A_   : Union[str, Any]						,	A_   : Dict						,	A_   : List[Tuple[str, str]]					)    ->  int:
  for from_key, to_key in keys:
    __UpperCAmelCase						    =       from_state_dict[from_key].clone()
    print(F'''Copied key={from_key} to={to_key}'''					)
  return to_state_dict




def       __lowerCAmelCase      (						A_   : str						,	A_   : Callable[[], nn.Module]						,	A_   : Callable[[], nn.Module]						,	A_   : RegNetConfig						,	A_   : Path						,	A_   : bool = True						,	)    ->  Tuple:
  print(F'''Converting {name}...'''					)
  with torch.no_grad():
    __UpperCAmelCase    ,		__UpperCAmelCase						    =       from_model_func()
    __UpperCAmelCase						    =       our_model_func(A_					).eval()
    __UpperCAmelCase						    =       ModuleTransfer(src=A_						,	dest=A_						,	raise_if_mismatch=A_					)
    __UpperCAmelCase						    =       torch.randn((1, 3, 2_24, 2_24)					)
    module_transfer(A_					)

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

  __UpperCAmelCase						    =       our_model(A_						,	output_hidden_states=A_					)
  __UpperCAmelCase						    =       (
      our_outputs.logits if isinstance(A_						,	A_					) else our_outputs.last_hidden_state
  )

  __UpperCAmelCase						    =       from_model(A_					)
  __UpperCAmelCase						    =       from_output[-1] if type(A_					) 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						    =       our_outputs.hidden_states[-1]

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

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

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




def       __lowerCAmelCase      (						A_   : Path						,	A_   : str = None						,	A_   : bool = True					)    ->  Union[str, Any]:
  __UpperCAmelCase						    =       "imagenet-1k-id2label.json"
  __UpperCAmelCase						    =       10_00
  __UpperCAmelCase						    =       (1, num_labels)

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

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

  __UpperCAmelCase						    =       partial(A_						,	num_labels=A_						,	idalabel=A_						,	labelaid=A_					)

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

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

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

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

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

  __UpperCAmelCase						    =       partial(
      A_						,	"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						    =       partial(
      A_						,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"						,	lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27						,	group_width=10_10						,	w_a=17_44						,	w_a=6_20.83						,	w_m=2.52					)					)					)						,	)

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

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

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

  __UpperCAmelCase						    =       partial(
      A_						,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"						,	lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27						,	group_width=10_10						,	w_a=17_44						,	w_a=6_20.83						,	w_m=2.52					)					)					)						,	)

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




from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_							=      logging.get_logger(__name__)

a_							=      {
    """facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	Dict    		= 'vit_mae'






  def __init__(				self:			List[Any]				,				__lowerCAmelCase:			Any=768				,				__lowerCAmelCase:			List[str]=12				,				__lowerCAmelCase:			Optional[int]=12				,				__lowerCAmelCase:			Tuple=3_072				,				__lowerCAmelCase:			List[Any]="gelu"				,				__lowerCAmelCase:			Dict=0.0				,				__lowerCAmelCase:			Tuple=0.0				,				__lowerCAmelCase:			Any=0.02				,				__lowerCAmelCase:			List[Any]=1E-12				,				__lowerCAmelCase:			List[str]=224				,				__lowerCAmelCase:			Optional[Any]=16				,				__lowerCAmelCase:			Union[str, Any]=3				,				__lowerCAmelCase:			Tuple=True				,				__lowerCAmelCase:			Union[str, Any]=16				,				__lowerCAmelCase:			Optional[int]=512				,				__lowerCAmelCase:			int=8				,				__lowerCAmelCase:			int=2_048				,				__lowerCAmelCase:			str=0.75				,				__lowerCAmelCase:			Union[str, Any]=False				,				**__lowerCAmelCase:			List[Any]				,				) -> Optional[int]:




    '''simple docstring'''




    super().__init__(**__lowerCAmelCase   )

    __UpperCAmelCase						    =       hidden_size
    __UpperCAmelCase						    =       num_hidden_layers
    __UpperCAmelCase						    =       num_attention_heads
    __UpperCAmelCase						    =       intermediate_size
    __UpperCAmelCase						    =       hidden_act
    __UpperCAmelCase						    =       hidden_dropout_prob
    __UpperCAmelCase						    =       attention_probs_dropout_prob
    __UpperCAmelCase						    =       initializer_range
    __UpperCAmelCase						    =       layer_norm_eps
    __UpperCAmelCase						    =       image_size
    __UpperCAmelCase						    =       patch_size
    __UpperCAmelCase						    =       num_channels
    __UpperCAmelCase						    =       qkv_bias
    __UpperCAmelCase						    =       decoder_num_attention_heads
    __UpperCAmelCase						    =       decoder_hidden_size
    __UpperCAmelCase						    =       decoder_num_hidden_layers
    __UpperCAmelCase						    =       decoder_intermediate_size
    __UpperCAmelCase						    =       mask_ratio
    __UpperCAmelCase						    =       norm_pix_loss



