'''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()
lowercase__  :      Optional[int]        =					logging.get_logger()







@dataclass
class lowerCamelCase							:




  '''simple docstring'''



  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	field(default_factory=lowerCamelCase					)
  lowerCAmelCase__										=	field(default_factory=lowerCamelCase					)






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       Tensor  ,							UpperCAmelCase__			:       Tensor  )				->str:
        UpperCAmelCase_            =  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			:       int  ,							UpperCAmelCase__			:       Tensor  )				->Dict:
        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  lowerCAmelCase__ (      self			:       str  )				->Any:
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda UpperCAmelCase__  : len(list(x.state_dict().keys()  )  ) > 0  ,							self.traced  )  )







@dataclass
class lowerCamelCase							:




  '''simple docstring'''



  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	1
  lowerCAmelCase__										=	field(default_factory=lowerCamelCase					)
  lowerCAmelCase__										=	field(default_factory=lowerCamelCase					)
  lowerCAmelCase__										=	True






  def __call__(      self			:       str  ,							UpperCAmelCase__			:       Tensor  )				->List[Any]:
        UpperCAmelCase_            =  Tracker(self.dest  )(UpperCAmelCase__  ).parametrized
        UpperCAmelCase_            =  Tracker(self.src  )(UpperCAmelCase__  ).parametrized

        UpperCAmelCase_            =  list(filter(lambda UpperCAmelCase__  : type(UpperCAmelCase__  ) not in self.src_skip  ,							UpperCAmelCase__  )  )
        UpperCAmelCase_            =  list(filter(lambda UpperCAmelCase__  : 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 lowerCamelCase							(	nn.Module					):




  '''simple docstring'''



  def __init__(      self			:       List[Any]  ,							UpperCAmelCase__			:       nn.Module  )				->List[str]:
        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(UpperCAmelCase__  ) + 1
              feature_blocks.append((f"""res{block_index}""", v)  )

        UpperCAmelCase_            =  nn.ModuleDict(UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Dict  ,							UpperCAmelCase__			:       Tensor  )				->Optional[Any]:
        return get_trunk_forward_outputs(
            UpperCAmelCase__  ,							out_feat_keys=UpperCAmelCase__  ,							feature_blocks=self._feature_blocks  ,							)







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       Dict  ,							UpperCAmelCase__			:       str  )				->str:
        UpperCAmelCase_            =  x.split('''-'''  )
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]  )






  def __getitem__(      self			:       int  ,							UpperCAmelCase__			:       str  )				->Callable[[], Tuple[nn.Module, Dict]]:
        # default to timm!
        if x not in self:
              UpperCAmelCase_            =  self.convert_name_to_timm(UpperCAmelCase__  )
              UpperCAmelCase_            =  partial(lambda: (timm.create_model(UpperCAmelCase__  ,							pretrained=UpperCAmelCase__  ).eval(), None)  )

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

        return val







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  def __getitem__(      self			:       Optional[int]  ,							UpperCAmelCase__			:       str  )				->Callable[[], nn.Module]:
        if "seer" in x and "in1k" not in x:
              UpperCAmelCase_            =  RegNetModel
        else:
              UpperCAmelCase_            =  RegNetForImageClassification
        return val






def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  List[Tuple[str, str]]					):


      '''simple docstring'''


      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       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Callable[[], nn.Module]							,    _UpperCamelCase  :  Callable[[], nn.Module]							,    _UpperCamelCase  :  RegNetConfig							,    _UpperCamelCase  :  Path							,    _UpperCamelCase  :  bool = True							,    ):


      '''simple docstring'''


      print(F"""Converting {name}..."""					)
      with torch.no_grad():
            UpperCAmelCase_      ,		UpperCAmelCase_            =  from_model_func()
            UpperCAmelCase_            =  our_model_func(_UpperCamelCase					).eval()
            UpperCAmelCase_            =  ModuleTransfer(src=_UpperCamelCase							,    dest=_UpperCamelCase							,    raise_if_mismatch=_UpperCamelCase					)
            UpperCAmelCase_            =  torch.randn((1, 3, 224, 224)					)
            module_transfer(_UpperCamelCase					)

      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(_UpperCamelCase							,    our_model.state_dict()							,    _UpperCamelCase					)
            our_model.load_state_dict(_UpperCamelCase					)

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

      UpperCAmelCase_            =  from_model(_UpperCamelCase					)
      UpperCAmelCase_            =  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_            =  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_            =  224 if '''seer''' not in name else 384
            # we can use the convnext one
            UpperCAmelCase_            =  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  __lowerCamelCase       (      _UpperCamelCase  :  Path							,    _UpperCamelCase  :  str = None							,    _UpperCamelCase  :  bool = True					):


      '''simple docstring'''


      UpperCAmelCase_            =  '''imagenet-1k-id2label.json'''
      UpperCAmelCase_            =  1000
      UpperCAmelCase_            =  (1, num_labels)

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

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

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

      UpperCAmelCase_            =  {
          '''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, 1_1110, 2_8280]							,    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, 1_1110, 2_8280]							,    groups_width=1010					),
      }

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

      def load_using_classy_vision(_UpperCamelCase  :  str							,    _UpperCamelCase  :  Callable[[], nn.Module]					) -> Tuple[nn.Module, Dict]:
            UpperCAmelCase_            =  torch.hub.load_state_dict_from_url(_UpperCamelCase							,    model_dir=str(_UpperCamelCase					)							,    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(_UpperCamelCase					)
            return model.eval(), model_state_dict["heads"]

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

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

      UpperCAmelCase_            =  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_            =  partial(
          _UpperCamelCase							,    '''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
      UpperCAmelCase_            =  partial(
          _UpperCamelCase							,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''							,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)							,    )

      UpperCAmelCase_            =  partial(
          _UpperCamelCase							,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''							,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)							,    )

      UpperCAmelCase_            =  partial(
          _UpperCamelCase							,    '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''							,    lambda: FakeRegNetVisslWrapper(RegNetYaaagf()					)							,    )

      UpperCAmelCase_            =  partial(
          _UpperCamelCase							,    '''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(
                _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__":
      lowercase__  :      int        =					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.",
      )

      lowercase__  :      Union[str, Any]        =					parser.parse_args()

      lowercase__  :      Path        =					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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__  :      Union[str, Any]        =					{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Union[str, Any]        =					["MobileViTFeatureExtractor"]
      lowercase__  :      List[Any]        =					["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Dict        =					[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Optional[int]        =					[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

      lowercase__  :      int        =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





