


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					:		Tuple            =						logging.get_logger()


@dataclass
class snake_case__			:
       _lowerCAmelCase   =42
       _lowerCAmelCase   =field(default_factory=UpperCamelCase_					)
       _lowerCAmelCase   =field(default_factory=UpperCamelCase_					)




       def     UpperCAmelCase__    ( self				:    Union[str, Any]    ,				_lowerCamelCase				:    Any    ,				_lowerCamelCase				:    Tensor    ,				_lowerCamelCase				:    Tensor			):
              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    ,				_lowerCamelCase				:    Tensor			):
              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     UpperCAmelCase__    ( self				:    List[Any]			):
              return list(filter(lambda _lowerCamelCase			: len(list(x.state_dict().keys()			)			) > 0    ,				self.traced			)			)


@dataclass
class snake_case__			:
       _lowerCAmelCase   =42
       _lowerCAmelCase   =42
       _lowerCAmelCase   =1
       _lowerCAmelCase   =field(default_factory=UpperCamelCase_					)
       _lowerCAmelCase   =field(default_factory=UpperCamelCase_					)
       _lowerCAmelCase   =True




       def __call__( self				:    List[Any]    ,				_lowerCamelCase				:    Tensor			):
              snake_case__ :       Optional[int]	   =							Tracker(self.dest			)(snake_case__			).parametrized
              snake_case__ :       int	   =							Tracker(self.src			)(snake_case__			).parametrized

              snake_case__ :       int	   =							list(filter(lambda _lowerCamelCase			: type(snake_case__			) not in self.src_skip    ,				snake_case__			)			)
              snake_case__ :       Any	   =							list(filter(lambda _lowerCamelCase			: 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 snake_case__			(    nn.Module					):




       def __init__( self				:    Any    ,				_lowerCamelCase				:    nn.Module			):
              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__ :       Dict	   =							len(snake_case__			) + 1
                     feature_blocks.append((F'''res{block_index}''', v)			)

              snake_case__ :       int	   =							nn.ModuleDict(snake_case__			)




       def     UpperCAmelCase__    ( self				:    Dict    ,				_lowerCamelCase				:    Tensor			):
              return get_trunk_forward_outputs(
                  snake_case__    ,				out_feat_keys=snake_case__    ,				feature_blocks=self._feature_blocks    ,				)


class snake_case__			(    UpperCamelCase_					):




       def     UpperCAmelCase__    ( self				:    Optional[Any]    ,				_lowerCamelCase				:    str			):
              snake_case__ :       Tuple	   =							x.split('-'			)
              return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]			)




       def __getitem__( self				:    str    ,				_lowerCamelCase				:    str			):
              # default to timm!
              if x not in self:
                     snake_case__ :       str	   =							self.convert_name_to_timm(snake_case__			)
                     snake_case__ :       List[Any]	   =							partial(lambda: (timm.create_model(snake_case__    ,				pretrained=snake_case__			).eval(), None)			)

              else:
                     snake_case__ :       Dict	   =							super().__getitem__(snake_case__			)

              return val


class snake_case__			(    UpperCamelCase_					):




       def __getitem__( self				:    Optional[int]    ,				_lowerCamelCase				:    str			):
              if "seer" in x and "in1k" not in x:
                     snake_case__ :       List[Any]	   =							RegNetModel
              else:
                     snake_case__ :       Union[str, Any]	   =							RegNetForImageClassification
              return val





def 	lowercase__(		A     ,			A     ,			A      ):



       for from_key, to_key in keys:
              snake_case__ :       int	   =							from_state_dict[from_key].clone()
              print(f'''Copied key={from_key} to={to_key}'''      )
       return to_state_dict





def 	lowercase__(		A     ,			A     ,			A     ,			A     ,			A     ,			A = True     ,			):
       print(f'''Converting {name}...'''      )
       with torch.no_grad():
              snake_case__ :       str	   =							from_model_func()
              snake_case__ :       Union[str, Any]	   =							our_model_func(A      ).eval()
              snake_case__ :       str	   =							ModuleTransfer(src=A     ,			dest=A     ,			raise_if_mismatch=A      )
              snake_case__ :       str	   =							torch.randn((1, 3, 2_2_4, 2_2_4)      )
              module_transfer(A      )

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

       snake_case__ :       List[str]	   =							our_model(A     ,			output_hidden_states=A      )
       snake_case__ :       List[Any]	   =							(
           our_outputs.logits if isinstance(A     ,			A      ) else our_outputs.last_hidden_state
       )

       snake_case__ :       List[str]	   =							from_model(A      )
       snake_case__ :       Any	   =							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:
              snake_case__ :       str	   =							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     ,			)

              snake_case__ :       List[str]	   =							2_2_4 if "seer" not in name else 3_8_4
              # we can use the convnext one
              snake_case__ :       Dict	   =							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 	lowercase__(		A     ,			A = None     ,			A = True      ):
       snake_case__ :       Union[str, Any]	   =							"imagenet-1k-id2label.json"
       snake_case__ :       Dict	   =							1_0_0_0
       snake_case__ :       int	   =							(1, num_labels)

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

       snake_case__ :       Dict	   =							idalabel
       snake_case__ :       str	   =							{v: k for k, v in idalabel.items()}

       snake_case__ :       Tuple	   =							partial(A     ,			num_labels=A     ,			idalabel=A     ,			labelaid=A      )

       snake_case__ :       List[Any]	   =							{
           "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      ),
       }

       snake_case__ :       str	   =							NameToOurModelFuncMap()
       snake_case__ :       Optional[Any]	   =							NameToFromModelFuncMap()
       # add seer weights logic

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

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

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

       snake_case__ :       List[Any]	   =							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()      )     ,			)

       snake_case__ :       Dict	   =							partial(
           A     ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'     ,			lambda: FakeRegNetVisslWrapper(
               RegNet(RegNetParams(depth=2_7     ,			group_width=1_0_1_0     ,			w_a=1_7_4_4     ,			w_a=620.83     ,			w_m=2.52      )      )      )     ,			)

       # IN1K finetuned
       snake_case__ :       int	   =							partial(
           A     ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )     ,			)

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

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

       snake_case__ :       str	   =							partial(
           A     ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'     ,			lambda: FakeRegNetVisslWrapper(
               RegNet(RegNetParams(depth=2_7     ,			group_width=1_0_1_0     ,			w_a=1_7_4_4     ,			w_a=620.83     ,			w_m=2.52      )      )      )     ,			)

       if model_name:
              convert_weight_and_push(
                  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__":
 lowerCamelCase					:		List[str]            =						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					:		Optional[Any]            =						parser.parse_args()

 lowerCamelCase					:		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_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase	:	Tuple				=      {
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase	:	int				=      ["""LxmertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase	:	Tuple				=      [
     """LxmertEncoder""",
     """LxmertForPreTraining""",
     """LxmertForQuestionAnswering""",
     """LxmertModel""",
     """LxmertPreTrainedModel""",
     """LxmertVisualFeatureEncoder""",
     """LxmertXLayer""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase	:	Optional[int]				=      [
     """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFLxmertForPreTraining""",
     """TFLxmertMainLayer""",
     """TFLxmertModel""",
     """TFLxmertPreTrainedModel""",
     """TFLxmertVisualFeatureEncoder""",
 ]


if TYPE_CHECKING:
 from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
 from .tokenization_lxmert import LxmertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_lxmert_fast import LxmertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_lxmert import (
      LxmertEncoder,
      LxmertForPreTraining,
      LxmertForQuestionAnswering,
      LxmertModel,
      LxmertPreTrainedModel,
      LxmertVisualFeatureEncoder,
      LxmertXLayer,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_lxmert import (
      TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLxmertForPreTraining,
      TFLxmertMainLayer,
      TFLxmertModel,
      TFLxmertPreTrainedModel,
      TFLxmertVisualFeatureEncoder,
  )

else:
 import sys

 _lowerCAmelCase	:	Any				=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


