





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()
_UpperCAmelCase     :   Union[str, Any]        =				logging.get_logger()



@dataclass
class 							_UpperCamelCase					:
       _UpperCamelCase				:					int         =   4_2
       _UpperCamelCase				:					List[str]         =   field(default_factory=lowercase__     )
       _UpperCamelCase				:					Union[str, Any]         =   field(default_factory=lowercase__     )

       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: Tensor					,   _SCREAMING_SNAKE_CASE: Tensor		)       -> Optional[Any]:




              """simple docstring"""


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

       def __call__(		self: List[str]					,   _SCREAMING_SNAKE_CASE: Tensor		)       -> Any:




              """simple docstring"""


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



       @property
       def lowercase (		self: List[str]		)       -> Optional[int]:




              """simple docstring"""


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



@dataclass
class 							_UpperCamelCase					:
       _UpperCamelCase				:					List[Any]         =   4_2
       _UpperCamelCase				:					Tuple         =   4_2
       _UpperCamelCase				:					Tuple         =   1
       _UpperCamelCase				:					int         =   field(default_factory=lowercase__     )
       _UpperCamelCase				:					List[str]         =   field(default_factory=lowercase__     )
       _UpperCamelCase				:					List[Any]         =   True

       def __call__(		self: int					,   _SCREAMING_SNAKE_CASE: Tensor		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  Tracker(self.dest		)(_SCREAMING_SNAKE_CASE		).parametrized
              UpperCamelCase_          =  Tracker(self.src		)(_SCREAMING_SNAKE_CASE		).parametrized

              UpperCamelCase_          =  list(filter(lambda _SCREAMING_SNAKE_CASE		: type(_SCREAMING_SNAKE_CASE		) not in self.src_skip					,   _SCREAMING_SNAKE_CASE		)		)
              UpperCamelCase_          =  list(filter(lambda _SCREAMING_SNAKE_CASE		: type(_SCREAMING_SNAKE_CASE		) not in self.dest_skip					,   _SCREAMING_SNAKE_CASE		)		)

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

              for dest_m, src_m in zip(_SCREAMING_SNAKE_CASE					,   _SCREAMING_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     ):

       def __init__(		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: nn.Module		)       -> int:




              """simple docstring"""


              super().__init__()

              UpperCamelCase_          =  []
              # - get the stem
              feature_blocks.append(("conv1", model.stem)		)
              # - get all the feature blocks
              for k, v in model.trunk_output.named_children():
                     assert k.startswith("block"		), f'''Unexpected layer name {k}'''
                     UpperCamelCase_          =  len(_SCREAMING_SNAKE_CASE		) + 1
                     feature_blocks.append((f'''res{block_index}''', v)		)

              UpperCamelCase_          =  nn.ModuleDict(_SCREAMING_SNAKE_CASE		)



       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Tensor		)       -> Optional[Any]:




              """simple docstring"""


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



class 							_UpperCamelCase					(	lowercase__     ):

       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: str		)       -> str:




              """simple docstring"""


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



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




              """simple docstring"""


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

              else:
                     UpperCamelCase_          =  super().__getitem__(_SCREAMING_SNAKE_CASE		)

              return val



class 							_UpperCamelCase					(	lowercase__     ):

       def __getitem__(		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: str		)       -> Callable[[], nn.Module]:




              """simple docstring"""


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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		int:


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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_ = True					,		)     ->		str:
       print(F'''Converting {name}...'''  )
       with torch.no_grad():
              UpperCamelCase_          =  from_model_func()
              UpperCamelCase_          =  our_model_func(_lowerCamelCase  ).eval()
              UpperCamelCase_          =  ModuleTransfer(src=_lowerCamelCase					,		dest=_lowerCamelCase					,		raise_if_mismatch=_lowerCamelCase  )
              UpperCamelCase_          =  torch.randn((1, 3, 224, 224)  )
              module_transfer(_lowerCamelCase  )

       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(_lowerCamelCase					,		our_model.state_dict()					,		_lowerCamelCase  )
              our_model.load_state_dict(_lowerCamelCase  )

       UpperCamelCase_          =  our_model(_lowerCamelCase					,		output_hidden_states=_lowerCamelCase  )
       UpperCamelCase_          =  (
           our_outputs.logits if isinstance(_lowerCamelCase					,		_lowerCamelCase  ) else our_outputs.last_hidden_state
       )

       UpperCamelCase_          =  from_model(_lowerCamelCase  )
       UpperCamelCase_          =  from_output[-1] if type(_lowerCamelCase  ) 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(_lowerCamelCase					,		_lowerCamelCase  ), "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=_lowerCamelCase					,		)

              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=_lowerCamelCase  )
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / name					,		commit_message="Add image processor"					,		use_temp_dir=_lowerCamelCase					,		)

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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_ = None					,		UpperCamelCase_ = True  )     ->		List[str]:
       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(_lowerCamelCase					,		_lowerCamelCase					,		repo_type="dataset"  )  )					,		"r"  )  )
       UpperCamelCase_          =  {int(_lowerCamelCase  ): v for k, v in idalabel.items()}

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

       UpperCamelCase_          =  partial(_lowerCamelCase					,		num_labels=_lowerCamelCase					,		idalabel=_lowerCamelCase					,		labelaid=_lowerCamelCase  )

       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, 11110, 28280]					,		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, 11110, 28280]					,		groups_width=1010  ),
       }

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

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

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

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

       UpperCamelCase_          =  partial(
           _lowerCamelCase					,		"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(
           _lowerCamelCase					,		"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(
           _lowerCamelCase					,		"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"					,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()  )					,		)

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

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

       UpperCamelCase_          =  partial(
           _lowerCamelCase					,		"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(
                  _lowerCamelCase					,		names_to_from_model_map[model_name]					,		names_to_ours_model_map[model_name]					,		names_to_config[model_name]					,		_lowerCamelCase					,		_lowerCamelCase					,		)
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(
                         _lowerCamelCase					,		names_to_from_model_map[model_name]					,		names_to_ours_model_map[model_name]					,		_lowerCamelCase					,		_lowerCamelCase					,		_lowerCamelCase					,		)
       return config, expected_shape


if __name__ == "__main__":
     _UpperCAmelCase     :   List[Any]        =				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.',
     )

     _UpperCAmelCase     :   Optional[int]        =				parser.parse_args()

     _UpperCAmelCase     :   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)








import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{'vocab_file': 'spiece.model'}

_UpperCAmelCase        =				{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

_UpperCAmelCase        =				{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
_UpperCAmelCase        =				0
_UpperCAmelCase        =				1
_UpperCAmelCase        =				2
_UpperCAmelCase        =				3
_UpperCAmelCase        =				4



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					List[str]         =   VOCAB_FILES_NAMES
       _UpperCamelCase				:					int         =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCamelCase				:					Any         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCamelCase				:					Dict         =   '''left'''

       def __init__(		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: Optional[int]=False					,   _SCREAMING_SNAKE_CASE: List[Any]=True					,   _SCREAMING_SNAKE_CASE: List[str]=False					,   _SCREAMING_SNAKE_CASE: Any="<s>"					,   _SCREAMING_SNAKE_CASE: Dict="</s>"					,   _SCREAMING_SNAKE_CASE: List[str]="<unk>"					,   _SCREAMING_SNAKE_CASE: Tuple="<sep>"					,   _SCREAMING_SNAKE_CASE: Optional[int]="<pad>"					,   _SCREAMING_SNAKE_CASE: int="<cls>"					,   _SCREAMING_SNAKE_CASE: Optional[Any]="<mask>"					,   _SCREAMING_SNAKE_CASE: List[str]=["<eop>", "<eod>"]					,   _SCREAMING_SNAKE_CASE: Optional[Dict[str, Any]] = None					,   **_SCREAMING_SNAKE_CASE: Optional[Any]					,   )       -> None:




              """simple docstring"""


              UpperCamelCase_          =  AddedToken(_SCREAMING_SNAKE_CASE					,   lstrip=_SCREAMING_SNAKE_CASE					,   rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		) else mask_token

              UpperCamelCase_          =  {} if sp_model_kwargs is None else sp_model_kwargs

              super().__init__(
                  do_lower_case=_SCREAMING_SNAKE_CASE					,   remove_space=_SCREAMING_SNAKE_CASE					,   keep_accents=_SCREAMING_SNAKE_CASE					,   bos_token=_SCREAMING_SNAKE_CASE					,   eos_token=_SCREAMING_SNAKE_CASE					,   unk_token=_SCREAMING_SNAKE_CASE					,   sep_token=_SCREAMING_SNAKE_CASE					,   pad_token=_SCREAMING_SNAKE_CASE					,   cls_token=_SCREAMING_SNAKE_CASE					,   mask_token=_SCREAMING_SNAKE_CASE					,   additional_special_tokens=_SCREAMING_SNAKE_CASE					,   sp_model_kwargs=self.sp_model_kwargs					,   **_SCREAMING_SNAKE_CASE					,   )

              UpperCamelCase_          =  3

              UpperCamelCase_          =  do_lower_case
              UpperCamelCase_          =  remove_space
              UpperCamelCase_          =  keep_accents
              UpperCamelCase_          =  vocab_file

              UpperCamelCase_          =  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
              self.sp_model.Load(_SCREAMING_SNAKE_CASE		)

       @property
       def lowercase (		self: List[Any]		)       -> Dict:




              """simple docstring"""


              return len(self.sp_model		)

       def lowercase (		self: List[Any]		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
              vocab.update(self.added_tokens_encoder		)
              return vocab

       def __getstate__(		self: int		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  self.__dict__.copy()
              UpperCamelCase_          =  None
              return state

       def __setstate__(		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  d

              # for backward compatibility
              if not hasattr(self					,   "sp_model_kwargs"		):
                     UpperCamelCase_          =  {}

              UpperCamelCase_          =  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
              self.sp_model.Load(self.vocab_file		)

       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> int:




              """simple docstring"""


              if self.remove_space:
                     UpperCamelCase_          =  " ".join(inputs.strip().split()		)
              else:
                     UpperCamelCase_          =  inputs
              UpperCamelCase_          =  outputs.replace("``"					,   "\""		).replace("''"					,   "\""		)

              if not self.keep_accents:
                     UpperCamelCase_          =  unicodedata.normalize("NFKD"					,   _SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  "".join([c for c in outputs if not unicodedata.combining(_SCREAMING_SNAKE_CASE		)]		)
              if self.do_lower_case:
                     UpperCamelCase_          =  outputs.lower()

              return outputs

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: str		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  self.preprocess_text(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  self.sp_model.encode(_SCREAMING_SNAKE_CASE					,   out_type=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  []
              for piece in pieces:
                     if len(_SCREAMING_SNAKE_CASE		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
                            UpperCamelCase_          =  self.sp_model.EncodeAsPieces(piece[:-1].replace(_SCREAMING_SNAKE_CASE					,   ""		)		)
                            if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                   if len(cur_pieces[0]		) == 1:
                                          UpperCamelCase_          =  cur_pieces[1:]
                                   else:
                                          UpperCamelCase_          =  cur_pieces[0][1:]
                            cur_pieces.append(piece[-1]		)
                            new_pieces.extend(_SCREAMING_SNAKE_CASE		)
                     else:
                            new_pieces.append(_SCREAMING_SNAKE_CASE		)

              return new_pieces

       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: str		)       -> Any:




              """simple docstring"""


              return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]		)       -> str:




              """simple docstring"""


              return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  "".join(_SCREAMING_SNAKE_CASE		).replace(_SCREAMING_SNAKE_CASE					,   " "		).strip()
              return out_string

       def lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: bool = False					,   _SCREAMING_SNAKE_CASE: bool = None					,   _SCREAMING_SNAKE_CASE: bool = True					,   **_SCREAMING_SNAKE_CASE: Dict					,   )       -> str:




              """simple docstring"""


              UpperCamelCase_          =  kwargs.pop("use_source_tokenizer"					,   _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE					,   skip_special_tokens=_SCREAMING_SNAKE_CASE		)

              # To avoid mixing byte-level and unicode for byte-level BPT
              # we need to build string separately for added tokens and byte-level tokens
              # cf. https://github.com/huggingface/transformers/issues/1133
              UpperCamelCase_          =  []
              UpperCamelCase_          =  []
              for token in filtered_tokens:
                     if skip_special_tokens and token in self.all_special_ids:
                            continue
                     if token in self.added_tokens_encoder:
                            if current_sub_text:
                                   sub_texts.append(self.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)		)
                                   UpperCamelCase_          =  []
                            sub_texts.append(_SCREAMING_SNAKE_CASE		)
                     else:
                            current_sub_text.append(_SCREAMING_SNAKE_CASE		)
              if current_sub_text:
                     sub_texts.append(self.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)		)

              # Mimic the behavior of the Rust tokenizer:
              # By default, there are no spaces between special tokens
              UpperCamelCase_          =  "".join(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  (
                  clean_up_tokenization_spaces
                  if clean_up_tokenization_spaces is not None
                  else self.clean_up_tokenization_spaces
              )
              if clean_up_tokenization_spaces:
                     UpperCamelCase_          =  self.clean_up_tokenization(_SCREAMING_SNAKE_CASE		)
                     return clean_text
              else:
                     return text

       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> List[int]:




              """simple docstring"""


              UpperCamelCase_          =  [self.sep_token_id]
              UpperCamelCase_          =  [self.cls_token_id]
              if token_ids_a is None:
                     return token_ids_a + sep + cls
              return token_ids_a + sep + token_ids_a + sep + cls

       def lowercase (		self: Dict					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None					,   _SCREAMING_SNAKE_CASE: bool = False		)       -> List[int]:




              """simple docstring"""


              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=_SCREAMING_SNAKE_CASE					,   token_ids_a=_SCREAMING_SNAKE_CASE					,   already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

              if token_ids_a is not None:
                     return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1, 1]
              return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1, 1]

       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> List[int]:




              """simple docstring"""


              UpperCamelCase_          =  [self.sep_token_id]
              UpperCamelCase_          =  [2]

              if token_ids_a is None:
                     return len(token_ids_a + sep		) * [0] + cls_segment_id
              return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id



       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Optional[str] = None		)       -> Tuple[str]:




              """simple docstring"""


              if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
                     logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
                     return
              UpperCamelCase_          =  os.path.join(
                  _SCREAMING_SNAKE_CASE					,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

              if os.path.abspath(self.vocab_file		) != os.path.abspath(_SCREAMING_SNAKE_CASE		) and os.path.isfile(self.vocab_file		):
                     copyfile(self.vocab_file					,   _SCREAMING_SNAKE_CASE		)
              elif not os.path.isfile(self.vocab_file		):
                     with open(_SCREAMING_SNAKE_CASE					,   "wb"		) as fi:
                            UpperCamelCase_          =  self.sp_model.serialized_model_proto()
                            fi.write(_SCREAMING_SNAKE_CASE		)

              return (out_vocab_file,)

