



'''simple docstring'''





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation





def   a_   (		_UpperCAmelCase    :      Optional[int]				)    ->  str:
      __snake_case :   Optional[int]													=    3_84
      __snake_case :   Optional[Any]													=    7
      if "tiny" in model_name:
            __snake_case :   Optional[int]													=    96
            __snake_case :   Any													=    (2, 2, 6, 2)
            __snake_case :   Any													=    (3, 6, 12, 24)
      elif "small" in model_name:
            __snake_case :   Optional[int]													=    96
            __snake_case :   Tuple													=    (2, 2, 18, 2)
            __snake_case :   List[str]													=    (3, 6, 12, 24)
      elif "base" in model_name:
            __snake_case :   Tuple													=    1_28
            __snake_case :   str													=    (2, 2, 18, 2)
            __snake_case :   Union[str, Any]													=    (4, 8, 16, 32)
            __snake_case :   Tuple													=    12
            __snake_case :   List[str]													=    5_12
      elif "large" in model_name:
            __snake_case :   Optional[int]													=    1_92
            __snake_case :   List[Any]													=    (2, 2, 18, 2)
            __snake_case :   int													=    (6, 12, 24, 48)
            __snake_case :   Optional[int]													=    12
            __snake_case :   Optional[int]													=    7_68

      # set label information
      __snake_case :   List[Any]													=    1_50
      __snake_case :   Optional[int]													=    'huggingface/label-files'
      __snake_case :   Union[str, Any]													=    'ade20k-id2label.json'
      __snake_case :   int													=    json.load(open(hf_hub_download(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset'				) ,'r'				)				)
      __snake_case :   Dict													=    {int(_UpperCAmelCase				): v for k, v in idalabel.items()}
      __snake_case :   List[str]													=    {v: k for k, v in idalabel.items()}

      __snake_case :   Optional[int]													=    SwinConfig(
          embed_dim=_UpperCAmelCase ,depths=_UpperCAmelCase ,num_heads=_UpperCAmelCase ,window_size=_UpperCAmelCase ,out_features=['stage1', 'stage2', 'stage3', 'stage4'] ,)
      __snake_case :   List[Any]													=    UperNetConfig(
          backbone_config=_UpperCAmelCase ,auxiliary_in_channels=_UpperCAmelCase ,num_labels=_UpperCAmelCase ,idalabel=_UpperCAmelCase ,labelaid=_UpperCAmelCase ,)

      return config





def   a_   (		_UpperCAmelCase    :      Tuple				)    ->  List[str]:
      __snake_case :   List[Any]													=    []

      # fmt: off
      # stem
      rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')				)
      rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')				)
      rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')				)
      rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias')				)
      # stages
      for i in range(len(config.backbone_config.depths				)				):
            for j in range(config.backbone_config.depths[i]				):
                  rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')				)
                  rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')				)
                  rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')				)
                  rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')				)
                  rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')				)
                  rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')				)
                  rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')				)
                  rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')				)
                  rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')				)
                  rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')				)
                  rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''')				)
                  rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''')				)

            if i < 3:
                  rename_keys.append((f'''backbone.stages.{i}.downsample.reduction.weight''', f'''backbone.encoder.layers.{i}.downsample.reduction.weight''')				)
                  rename_keys.append((f'''backbone.stages.{i}.downsample.norm.weight''', f'''backbone.encoder.layers.{i}.downsample.norm.weight''')				)
                  rename_keys.append((f'''backbone.stages.{i}.downsample.norm.bias''', f'''backbone.encoder.layers.{i}.downsample.norm.bias''')				)
            rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''')				)
            rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''')				)

      # decode head
      rename_keys.extend(
          [
              ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
              ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
              ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
              ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
          ]				)
      # fmt: on

      return rename_keys





def   a_   (		_UpperCAmelCase    :      Optional[Any] ,_UpperCAmelCase    :      Union[str, Any] ,_UpperCAmelCase    :      str				)    ->  Dict:
      __snake_case :   Dict													=    dct.pop(_UpperCAmelCase				)
      __snake_case :   List[Any]													=    val





def   a_   (		_UpperCAmelCase    :      List[Any] ,_UpperCAmelCase    :      str				)    ->  Any:
      __snake_case :   Optional[Any]													=    [int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
      for i in range(len(backbone_config.depths				)				):
            __snake_case :   Dict													=    num_features[i]
            for j in range(backbone_config.depths[i]				):
                  # fmt: off
                  # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                  __snake_case :   Optional[int]													=    state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''				)
                  __snake_case :   int													=    state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'''				)
                  # next, add query, keys and values (in that order) to the state dict
                  __snake_case :   List[str]													=    in_proj_weight[:dim, :]
                  __snake_case :   Any													=    in_proj_bias[: dim]
                  __snake_case :   List[Any]													=    in_proj_weight[
                      dim : dim * 2, :
                  ]
                  __snake_case :   List[str]													=    in_proj_bias[
                      dim : dim * 2
                  ]
                  __snake_case :   Any													=    in_proj_weight[
                      -dim :, :
                  ]
                  __snake_case :   List[str]													=    in_proj_bias[-dim :]
                  # fmt: on





def   a_   (		_UpperCAmelCase    :      Tuple				)    ->  Union[str, Any]:
      __snake_case							,     __snake_case :   Union[str, Any]													=    x.shape
      __snake_case :   List[str]													=    x.reshape(_UpperCAmelCase ,4 ,in_channel // 4				)
      __snake_case :   Any													=    x[:, [0, 2, 1, 3], :].transpose(1 ,2				).reshape(_UpperCAmelCase ,_UpperCAmelCase				)
      return x





def   a_   (		_UpperCAmelCase    :      int				)    ->  Any:
      __snake_case							,     __snake_case :   List[str]													=    x.shape
      __snake_case :   Dict													=    x.reshape(_UpperCAmelCase ,in_channel // 4 ,4				)
      __snake_case :   List[Any]													=    x[:, :, [0, 2, 1, 3]].transpose(1 ,2				).reshape(_UpperCAmelCase ,_UpperCAmelCase				)

      return x





def   a_   (		_UpperCAmelCase    :      Optional[Any]				)    ->  Optional[int]:
      __snake_case :   Any													=    x.shape[0]
      __snake_case :   Optional[int]													=    x.reshape(4 ,in_channel // 4				)
      __snake_case :   int													=    x[[0, 2, 1, 3], :].transpose(0 ,1				).reshape(_UpperCAmelCase				)
      return x





def   a_   (		_UpperCAmelCase    :      int				)    ->  Tuple:
      __snake_case :   Optional[int]													=    x.shape[0]
      __snake_case :   List[str]													=    x.reshape(in_channel // 4 ,4				)
      __snake_case :   List[Any]													=    x[:, [0, 2, 1, 3]].transpose(0 ,1				).reshape(_UpperCAmelCase				)
      return x





def   a_   (		_UpperCAmelCase    :      Union[str, Any] ,_UpperCAmelCase    :      Optional[int] ,_UpperCAmelCase    :      List[Any]				)    ->  Any:
      __snake_case :   Optional[int]													=    {
          'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
          'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
          'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
          'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
      }
      __snake_case :   Union[str, Any]													=    model_name_to_url[model_name]
      __snake_case :   Optional[int]													=    torch.hub.load_state_dict_from_url(_UpperCAmelCase ,map_location='cpu' ,file_name=_UpperCAmelCase				)[
          'state_dict'
      ]

      for name, param in state_dict.items():
            print(_UpperCAmelCase ,param.shape				)

      __snake_case :   Optional[Any]													=    get_upernet_config(_UpperCAmelCase				)
      __snake_case :   List[Any]													=    UperNetForSemanticSegmentation(_UpperCAmelCase				)
      model.eval()

      # replace "bn" => "batch_norm"
      for key in state_dict.copy().keys():
            __snake_case :   Optional[int]													=    state_dict.pop(_UpperCAmelCase				)
            if "bn" in key:
                  __snake_case :   Dict													=    key.replace('bn' ,'batch_norm'				)
            __snake_case :   str													=    val

      # rename keys
      __snake_case :   int													=    create_rename_keys(_UpperCAmelCase				)
      for src, dest in rename_keys:
            rename_key(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase				)
      read_in_q_k_v(_UpperCAmelCase ,config.backbone_config				)

      # fix downsample parameters
      for key, value in state_dict.items():
            if "downsample" in key:
                  if "reduction" in key:
                        __snake_case :   Tuple													=    reverse_correct_unfold_reduction_order(_UpperCAmelCase				)
                  if "norm" in key:
                        __snake_case :   Optional[Any]													=    reverse_correct_unfold_norm_order(_UpperCAmelCase				)

      model.load_state_dict(_UpperCAmelCase				)

      # verify on image
      __snake_case :   Optional[int]													=    'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
      __snake_case :   Optional[int]													=    Image.open(requests.get(_UpperCAmelCase ,stream=_UpperCAmelCase				).raw				).convert('RGB'				)

      __snake_case :   Union[str, Any]													=    SegformerImageProcessor()
      __snake_case :   List[str]													=    processor(_UpperCAmelCase ,return_tensors='pt'				).pixel_values

      with torch.no_grad():
            __snake_case :   Union[str, Any]													=    model(_UpperCAmelCase				)
            __snake_case :   Dict													=    outputs.logits

      print(logits.shape				)
      print('First values of logits:' ,logits[0, 0, :3, :3]				)
      # assert values
      if model_name == "upernet-swin-tiny":
            __snake_case :   Dict													=    torch.tensor(
                [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]				)
      elif model_name == "upernet-swin-small":
            __snake_case :   int													=    torch.tensor(
                [[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]]				)
      elif model_name == "upernet-swin-base":
            __snake_case :   int													=    torch.tensor(
                [[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]]				)
      elif model_name == "upernet-swin-large":
            __snake_case :   int													=    torch.tensor(
                [[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]]				)
      print('Logits:' ,outputs.logits[0, 0, :3, :3]				)
      assert torch.allclose(outputs.logits[0, 0, :3, :3] ,_UpperCAmelCase ,atol=1E-4				)
      print('Looks ok!'				)

      if pytorch_dump_folder_path is not None:
            print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''				)
            model.save_pretrained(_UpperCAmelCase				)
            print(f'''Saving processor to {pytorch_dump_folder_path}'''				)
            processor.save_pretrained(_UpperCAmelCase				)

      if push_to_hub:
            print(f'''Pushing model and processor for {model_name} to hub'''				)
            model.push_to_hub(f'''openmmlab/{model_name}'''				)
            processor.push_to_hub(f'''openmmlab/{model_name}'''				)


if __name__ == "__main__":
    A__    :   str           =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--model_name''',
        default='''upernet-swin-tiny''',
        type=str,
        choices=[F"""upernet-swin-{size}""" for size in ['''tiny''', '''small''', '''base''', '''large''']],
        help='''Name of the Swin + UperNet model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )
    parser.add_argument(
        '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
    )

    A__    :   Union[str, Any]           =					parser.parse_args()
    convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''





import os
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 logging


A__    :   Tuple           =					logging.get_logger(__name__)

A__    :   int           =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

A__    :   int           =					{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

A__    :   List[str]           =					{
    '''camembert-base''': 5_1_2,
}

A__    :   str           =					'''▁'''



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= VOCAB_FILES_NAMES
 A__				= PRETRAINED_VOCAB_FILES_MAP
 A__				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__				= ['''input_ids''', '''attention_mask''']
 def __init__(   self						:							List[Any]      ,     __a						:							int      ,     __a						:							str="<s>"      ,     __a						:							Dict="</s>"      ,     __a						:							Union[str, Any]="</s>"      ,     __a						:							List[str]="<s>"      ,     __a						:							Optional[int]="<unk>"      ,     __a						:							Optional[Any]="<pad>"      ,     __a						:							str="<mask>"      ,     __a						:							Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]      ,     __a						:							Optional[Dict[str, Any]] = None      ,     **__a						:							Any      ,     )      ->   None:







       '''simple docstring'''
       # Mask token behave like a normal word, i.e. include the space before it
       __snake_case :   Optional[int]													=    AddedToken(__a      ,     lstrip=__a      ,     rstrip=__a  ) if isinstance(__a      ,     __a  ) else mask_token

       __snake_case :   Optional[Any]													=    {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=__a      ,     eos_token=__a      ,     unk_token=__a      ,     sep_token=__a      ,     cls_token=__a      ,     pad_token=__a      ,     mask_token=__a      ,     additional_special_tokens=__a      ,     sp_model_kwargs=self.sp_model_kwargs      ,     **__a      ,     )
       __snake_case :   Optional[Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(str(__a  )  )
       __snake_case :   str													=    vocab_file
       # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
       # sentencepiece vocabulary (this is the case for <s> and </s>
       __snake_case :   Any													=    {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
       __snake_case :   Optional[int]													=    len(self.fairseq_tokens_to_ids  )
       __snake_case :   str													=    len(self.sp_model  ) + len(self.fairseq_tokens_to_ids  )
       __snake_case :   Dict													=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def A_   (   self						:							List[str]      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''

       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __snake_case :   Dict													=    [self.cls_token_id]
       __snake_case :   Optional[int]													=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep
 def A_   (   self						:							Optional[Any]      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None      ,     __a						:							bool = False  )      ->   List[int]:







       '''simple docstring'''
       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__a      ,     token_ids_a=__a      ,     already_has_special_tokens=__a  )

       if token_ids_a is None:
             return [1] + ([0] * len(__a  )) + [1]
       return [1] + ([0] * len(__a  )) + [1, 1] + ([0] * len(__a  )) + [1]
 def A_   (   self						:							int      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    [self.sep_token_id]
       __snake_case :   Optional[int]													=    [self.cls_token_id]

       if token_ids_a is None:
             return len(cls + token_ids_a + sep  ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]
 @property
 def A_   (   self						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       return len(self.fairseq_tokens_to_ids  ) + len(self.sp_model  )
 def A_   (   self						:							List[str]  )      ->   str:







       '''simple docstring'''
       __snake_case :   Any													=    {self.convert_ids_to_tokens(__a  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab
 def A_   (   self						:							Dict      ,     __a						:							str  )      ->   List[str]:







       '''simple docstring'''
       return self.sp_model.encode(__a      ,     out_type=__a  )
 def A_   (   self						:							Optional[Any]      ,     __a						:							List[str]  )      ->   List[Any]:







       '''simple docstring'''
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       elif self.sp_model.PieceToId(__a  ) == 0:
             # Convert sentence piece unk token to fairseq unk token index
             return self.unk_token_id
       return self.fairseq_offset + self.sp_model.PieceToId(__a  )
 def A_   (   self						:							Optional[int]      ,     __a						:							List[Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset  )
 def A_   (   self						:							Dict      ,     __a						:							Union[str, Any]  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Tuple													=    []
       __snake_case :   List[Any]													=    ''
       __snake_case :   Optional[int]													=    False
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   if not prev_is_special:
                         out_string += " "
                   out_string += self.sp_model.decode(__a  ) + token
                   __snake_case :   Optional[int]													=    True
                   __snake_case :   Any													=    []
             else:
                   current_sub_tokens.append(__a  )
                   __snake_case :   Dict													=    False
       out_string += self.sp_model.decode(__a  )
       return out_string.strip()
 def __getstate__(   self						:							str  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    self.__dict__.copy()
       __snake_case :   int													=    None
       return state
 def __setstate__(   self						:							List[str]      ,     __a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   List[str]													=    d

       # for backward compatibility
       if not hasattr(self      ,     'sp_model_kwargs'  ):
             __snake_case :   Dict													=    {}

       __snake_case :   Optional[Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(self.vocab_file  )





 def A_   (   self						:							Tuple      ,     __a						:							str      ,     __a						:							Optional[str] = None  )      ->   Tuple[str]:







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

       if os.path.abspath(self.vocab_file  ) != os.path.abspath(__a  ) and os.path.isfile(self.vocab_file  ):
             copyfile(self.vocab_file      ,     __a  )
       elif not os.path.isfile(self.vocab_file  ):
             with open(__a      ,     'wb'  ) as fi:
                   __snake_case :   Union[str, Any]													=    self.sp_model.serialized_model_proto()
                   fi.write(__a  )

       return (out_vocab_file,)



