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     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= 42
 UpperCAmelCase_     			= field(default_factory=A__						)
 UpperCAmelCase_     			= field(default_factory=A__						)




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Dict      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = len(list(m.modules())) == 1 or isinstance(__A      ,   nn.Convad) or isinstance(__A      ,   nn.BatchNormad)
        if has_not_submodules:
               self.traced.append(__A)




 def __call__(      self :  int      ,   lowercase__ :  List[Any]):







        '''simple docstring'''



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




 @property
 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



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






@dataclass
class     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= 42
 UpperCAmelCase_     			= 42
 UpperCAmelCase_     			= 1
 UpperCAmelCase_     			= field(default_factory=A__						)
 UpperCAmelCase_     			= field(default_factory=A__						)
 UpperCAmelCase_     			= True




 def __call__(      self :  Dict      ,   lowercase__ :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = Tracker(self.dest)(__A).parametrized
        lowerCAmelCase__					     = Tracker(self.src)(__A).parametrized

        lowerCAmelCase__					     = list(filter(lambda lowercase__: type(__A) not in self.src_skip      ,   __A))
        lowerCAmelCase__					     = list(filter(lambda lowercase__: type(__A) not in self.dest_skip      ,   __A))

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

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






class     a_			(						nn.Module						):






 '''simple docstring'''






 def __init__(      self :  int      ,   lowercase__ :  str):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__					     = []
        # - 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}"""
               lowerCAmelCase__					     = len(__A) + 1
               feature_blocks.append((F"""res{block_index}""", v))

        lowerCAmelCase__					     = nn.ModuleDict(__A)




 def 		__snake_case						(      self :  str      ,   lowercase__ :  str):







        '''simple docstring'''



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






class     a_			(						A__						):






 '''simple docstring'''






 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  Optional[Any]):







        '''simple docstring'''



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




 def __getitem__(      self :  Any      ,   lowercase__ :  Optional[int]):







        '''simple docstring'''



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

        else:
               lowerCAmelCase__					     = super().__getitem__(__A)

        return val






class     a_			(						A__						):






 '''simple docstring'''






 def __getitem__(      self :  Optional[int]      ,   lowercase__ :  Union[str, Any]):







        '''simple docstring'''



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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):





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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = True     ,  ):
       print(F"""Converting {name}..."""     )
       with torch.no_grad():
              lowerCAmelCase__					     = from_model_func()
              lowerCAmelCase__					     = our_model_func(lowercase__     ).eval()
              lowerCAmelCase__					     = ModuleTransfer(src=lowercase__     ,  dest=lowercase__     ,  raise_if_mismatch=lowercase__     )
              lowerCAmelCase__					     = torch.randn((1, 3, 2_2_4, 2_2_4)     )
              module_transfer(lowercase__     )

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

       lowerCAmelCase__					     = our_model(lowercase__     ,  output_hidden_states=lowercase__     )
       lowerCAmelCase__					     = (
           our_outputs.logits if isinstance(lowercase__     ,  lowercase__     ) else our_outputs.last_hidden_state
       )

       lowerCAmelCase__					     = from_model(lowercase__     )
       lowerCAmelCase__					     = from_output[-1] if type(lowercase__     ) 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:
              lowerCAmelCase__					     = our_outputs.hidden_states[-1]

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

              lowerCAmelCase__					     = 2_2_4 if """seer""" not in name else 3_8_4
              # we can use the convnext one
              lowerCAmelCase__					     = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'     ,  size=lowercase__     )
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / name     ,  commit_message='Add image processor'     ,  use_temp_dir=lowercase__     ,  )

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__ = None     ,  lowerCAmelCase__ = True     ):
       lowerCAmelCase__					     = """imagenet-1k-id2label.json"""
       lowerCAmelCase__					     = 1_0_0_0
       lowerCAmelCase__					     = (1, num_labels)

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

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

       lowerCAmelCase__					     = partial(lowercase__     ,  num_labels=lowercase__     ,  idalabel=lowercase__     ,  labelaid=lowercase__     )

       lowerCAmelCase__					     = {
           """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     ),
       }

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

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

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

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

       lowerCAmelCase__					     = partial(
           lowercase__     ,  '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()     )     ,  )

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

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

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

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

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

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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__     						=			{
    'configuration_x_clip': [
        'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XCLIPConfig',
        'XCLIPTextConfig',
        'XCLIPVisionConfig',
    ],
    'processing_x_clip': ['XCLIPProcessor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'XCLIPModel',
        'XCLIPPreTrainedModel',
        'XCLIPTextModel',
        'XCLIPVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_x_clip import (
        XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        XCLIPConfig,
        XCLIPTextConfig,
        XCLIPVisionConfig,
    )
    from .processing_x_clip import XCLIPProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_x_clip import (
            XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            XCLIPModel,
            XCLIPPreTrainedModel,
            XCLIPTextModel,
            XCLIPVisionModel,
        )

else:
    import sys

    lowerCAmelCase__     						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
