
'''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()
SCREAMING_SNAKE_CASE     =       logging.get_logger()






@dataclass
class 							UpperCAmelCase_							:

 '''simple docstring'''
 lowercase_					:    nn.Module
 lowercase_					:    List[nn.Module]				=							field(default_factory=A				)
 lowercase_					:    list				=							field(default_factory=A				)







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  str			,				snake_case__		:  Tensor			,				snake_case__		:  Tensor ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							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		:  str			,				snake_case__		:  Tensor ):



   '''simple docstring'''




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







 @property
 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




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






@dataclass
class 							UpperCAmelCase_							:

 '''simple docstring'''
 lowercase_					:    nn.Module
 lowercase_					:    nn.Module
 lowercase_					:    int				=							1
 lowercase_					:    List				=							field(default_factory=A				)
 lowercase_					:    List				=							field(default_factory=A				)
 lowercase_					:    bool				=							True







 def __call__(					self		:  int			,				snake_case__		:  Tensor ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							Tracker(self.dest )(snake_case__ ).parametrized
   UpperCAmelCase__      :					Optional[int]				       =							Tracker(self.src )(snake_case__ ).parametrized

   UpperCAmelCase__      :					List[Any]				       =							list(filter(lambda snake_case__ : type(snake_case__ ) not in self.src_skip			,				snake_case__ ) )
   UpperCAmelCase__      :					Any				       =							list(filter(lambda snake_case__ : type(snake_case__ ) not in self.dest_skip			,				snake_case__ ) )

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

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






class 							UpperCAmelCase_							(    nn.Module				):

 '''simple docstring'''







 def __init__(					self		:  str			,				snake_case__		:  nn.Module ):



   '''simple docstring'''




   super().__init__()

   UpperCAmelCase__      :					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}"""
     UpperCAmelCase__      :					Optional[int]				       =							len(snake_case__ ) + 1
     feature_blocks.append((F"""res{block_index}""", v) )

   UpperCAmelCase__      :					Any				       =							nn.ModuleDict(snake_case__ )







 def 							UpperCamelCase  (					self		:  int			,				snake_case__		:  Tensor ):



   '''simple docstring'''




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






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  str ):



   '''simple docstring'''




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







 def __getitem__(					self		:  List[str]			,				snake_case__		:  str ):



   '''simple docstring'''




   if x not in self:
     UpperCAmelCase__      :					Dict				       =							self.convert_name_to_timm(snake_case__ )
     UpperCAmelCase__      :					Optional[int]				       =							partial(lambda: (timm.create_model(snake_case__			,				pretrained=snake_case__ ).eval(), None) )

   else:
     UpperCAmelCase__      :					Optional[Any]				       =							super().__getitem__(snake_case__ )

   return val






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''







 def __getitem__(					self		:  Any			,				snake_case__		:  str ):



   '''simple docstring'''




   if "seer" in x and "in1k" not in x:
     UpperCAmelCase__      :					Tuple				       =							RegNetModel
   else:
     UpperCAmelCase__      :					Optional[int]				       =							RegNetForImageClassification
   return val







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__		):
  for from_key, to_key in keys:
    UpperCAmelCase__      :					Optional[int]				       =							from_state_dict[from_key].clone()
    print(F"""Copied key={from_key} to={to_key}"""		)
  return to_state_dict







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

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

  UpperCAmelCase__      :					Optional[int]				       =							our_model(lowercase__   ,       output_hidden_states=lowercase__		)
  UpperCAmelCase__      :					str				       =							(
      our_outputs.logits if isinstance(lowercase__   ,       lowercase__		) else our_outputs.last_hidden_state
  )

  UpperCAmelCase__      :					Dict				       =							from_model(lowercase__		)
  UpperCAmelCase__      :					Tuple				       =							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:
    UpperCAmelCase__      :					Any				       =							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__   ,       )

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

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

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

  UpperCAmelCase__      :					int				       =							partial(lowercase__   ,       num_labels=lowercase__   ,       idalabel=lowercase__   ,       labelaid=lowercase__		)

  UpperCAmelCase__      :					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		),
  }

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

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

  # pretrained
  UpperCAmelCase__      :					Optional[int]				       =							partial(
      lowercase__   ,       "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"   ,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)   ,       )

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

  UpperCAmelCase__      :					Optional[Any]				       =							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()		)   ,       )

  UpperCAmelCase__      :					List[Any]				       =							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=620.83   ,       w_m=2.52		)		)		)   ,       )

  # IN1K finetuned
  UpperCAmelCase__      :					Optional[int]				       =							partial(
      lowercase__   ,       "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"   ,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)   ,       )

  UpperCAmelCase__      :					Optional[Any]				       =							partial(
      lowercase__   ,       "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"   ,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)   ,       )

  UpperCAmelCase__      :					Optional[int]				       =							partial(
      lowercase__   ,       "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"   ,       lambda: FakeRegNetVisslWrapper(RegNetYaaagf()		)   ,       )

  UpperCAmelCase__      :					int				       =							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=620.83   ,       w_m=2.52		)		)		)   ,       )

  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__":
      SCREAMING_SNAKE_CASE     =       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.""",
      )

      SCREAMING_SNAKE_CASE     =       parser.parse_args()

      SCREAMING_SNAKE_CASE     =       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_sentencepiece_available


SCREAMING_SNAKE_CASE     =       {}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       ["""GPTSw3Tokenizer"""]


if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpt_swa import GPTSwaTokenizer

else:
      import sys

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



