



"""simple docstring"""
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def    __magic_name__ (							lowercase							,	lowercase=1       ):





   if n_shave_prefix_segments >= 0:
      return ".".join(path.split("""."""       )[n_shave_prefix_segments:]       )
   else:
      return ".".join(path.split("""."""       )[:n_shave_prefix_segments]       )





def    __magic_name__ (							lowercase							,	lowercase=0       ):
   SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=[]
   for old_item in old_list:
      SCREAMING_SNAKE_CASE_:       Optional[Any]   	=old_item.replace("""in_layers.0"""							,	"""norm1"""       )
      SCREAMING_SNAKE_CASE_:       Optional[int]   	=new_item.replace("""in_layers.2"""							,	"""conv1"""       )

      SCREAMING_SNAKE_CASE_:       Dict   	=new_item.replace("""out_layers.0"""							,	"""norm2"""       )
      SCREAMING_SNAKE_CASE_:       str   	=new_item.replace("""out_layers.3"""							,	"""conv2"""       )

      SCREAMING_SNAKE_CASE_:       str   	=new_item.replace("""emb_layers.1"""							,	"""time_emb_proj"""       )
      SCREAMING_SNAKE_CASE_:       Optional[Any]   	=new_item.replace("""skip_connection"""							,	"""conv_shortcut"""       )

      SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=shave_segments(lowercase							,	n_shave_prefix_segments=lowercase       )

      mapping.append({"""old""": old_item, """new""": new_item}       )

   return mapping





def    __magic_name__ (							lowercase							,	lowercase=0       ):
   SCREAMING_SNAKE_CASE_:       int   	=[]
   for old_item in old_list:
      SCREAMING_SNAKE_CASE_:       List[str]   	=old_item

      SCREAMING_SNAKE_CASE_:       int   	=new_item.replace("""norm.weight"""							,	"""group_norm.weight"""       )
      SCREAMING_SNAKE_CASE_:       Optional[Any]   	=new_item.replace("""norm.bias"""							,	"""group_norm.bias"""       )

      SCREAMING_SNAKE_CASE_:       Optional[Any]   	=new_item.replace("""proj_out.weight"""							,	"""proj_attn.weight"""       )
      SCREAMING_SNAKE_CASE_:       int   	=new_item.replace("""proj_out.bias"""							,	"""proj_attn.bias"""       )

      SCREAMING_SNAKE_CASE_:       str   	=shave_segments(lowercase							,	n_shave_prefix_segments=lowercase       )

      mapping.append({"""old""": old_item, """new""": new_item}       )

   return mapping





def    __magic_name__ (							lowercase							,	lowercase							,	lowercase							,	lowercase=None							,	lowercase=None							,	lowercase=None       ):
   assert isinstance(lowercase							,	lowercase       ), "Paths should be a list of dicts containing 'old' and 'new' keys."

   # Splits the attention layers into three variables.
   if attention_paths_to_split is not None:
      for path, path_map in attention_paths_to_split.items():
         SCREAMING_SNAKE_CASE_:       Any   	=old_checkpoint[path]
         SCREAMING_SNAKE_CASE_:       int   	=old_tensor.shape[0] // 3

         SCREAMING_SNAKE_CASE_:       int   	=(-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

         SCREAMING_SNAKE_CASE_:       Tuple   	=old_tensor.shape[0] // config["""num_head_channels"""] // 3

         SCREAMING_SNAKE_CASE_:       List[Any]   	=old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]       )
         SCREAMING_SNAKE_CASE_:       str   	=old_tensor.split(channels // num_heads							,	dim=1       )

         SCREAMING_SNAKE_CASE_:       int   	=query.reshape(lowercase       )
         SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=key.reshape(lowercase       )
         SCREAMING_SNAKE_CASE_:       Optional[int]   	=value.reshape(lowercase       )

   for path in paths:
      SCREAMING_SNAKE_CASE_:       Any   	=path["""new"""]

      # These have already been assigned
      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      # Global renaming happens here
      SCREAMING_SNAKE_CASE_:       Any   	=new_path.replace("""middle_block.0"""							,	"""mid_block.resnets.0"""       )
      SCREAMING_SNAKE_CASE_:       Any   	=new_path.replace("""middle_block.1"""							,	"""mid_block.attentions.0"""       )
      SCREAMING_SNAKE_CASE_:       Any   	=new_path.replace("""middle_block.2"""							,	"""mid_block.resnets.1"""       )

      if additional_replacements is not None:
         for replacement in additional_replacements:
            SCREAMING_SNAKE_CASE_:       Any   	=new_path.replace(replacement["""old"""]							,	replacement["""new"""]       )

        # proj_attn.weight has to be converted from conv 1D to linear
      if "proj_attn.weight" in new_path:
         SCREAMING_SNAKE_CASE_:       List[Any]   	=old_checkpoint[path["""old"""]][:, :, 0]
      else:
         SCREAMING_SNAKE_CASE_:       Dict   	=old_checkpoint[path["""old"""]]





def    __magic_name__ (							lowercase							,	lowercase       ):
   SCREAMING_SNAKE_CASE_:       str   	={}

   SCREAMING_SNAKE_CASE_:       str   	=checkpoint["""time_embed.0.weight"""]
   SCREAMING_SNAKE_CASE_:       List[Any]   	=checkpoint["""time_embed.0.bias"""]
   SCREAMING_SNAKE_CASE_:       int   	=checkpoint["""time_embed.2.weight"""]
   SCREAMING_SNAKE_CASE_:       List[str]   	=checkpoint["""time_embed.2.bias"""]

   SCREAMING_SNAKE_CASE_:       str   	=checkpoint["""input_blocks.0.0.weight"""]
   SCREAMING_SNAKE_CASE_:       Any   	=checkpoint["""input_blocks.0.0.bias"""]

   SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=checkpoint["""out.0.weight"""]
   SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=checkpoint["""out.0.bias"""]
   SCREAMING_SNAKE_CASE_:       str   	=checkpoint["""out.2.weight"""]
   SCREAMING_SNAKE_CASE_:       Tuple   	=checkpoint["""out.2.bias"""]

   # Retrieves the keys for the input blocks only
   SCREAMING_SNAKE_CASE_:       Optional[Any]   	=len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """input_blocks""" in layer}       )
   SCREAMING_SNAKE_CASE_:       Optional[Any]   	={
       layer_id: [key for key in checkpoint if f'''input_blocks.{layer_id}''' in key]
       for layer_id in range(lowercase       )
   }

   # Retrieves the keys for the middle blocks only
   SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """middle_block""" in layer}       )
   SCREAMING_SNAKE_CASE_:       Union[str, Any]   	={
       layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
       for layer_id in range(lowercase       )
   }

   # Retrieves the keys for the output blocks only
   SCREAMING_SNAKE_CASE_:       List[str]   	=len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """output_blocks""" in layer}       )
   SCREAMING_SNAKE_CASE_:       List[Any]   	={
       layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
       for layer_id in range(lowercase       )
   }

   for i in range(1							,	lowercase       ):
      SCREAMING_SNAKE_CASE_:       Dict   	=(i - 1) // (config["""num_res_blocks"""] + 1)
      SCREAMING_SNAKE_CASE_:       Tuple   	=(i - 1) % (config["""num_res_blocks"""] + 1)

      SCREAMING_SNAKE_CASE_:       Optional[int]   	=[key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
      SCREAMING_SNAKE_CASE_:       Optional[Any]   	=[key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]

      if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
         SCREAMING_SNAKE_CASE_:       Optional[int]   	=checkpoint[
             f'''input_blocks.{i}.0.op.weight'''
         ]
         SCREAMING_SNAKE_CASE_:       Tuple   	=checkpoint[
             f'''input_blocks.{i}.0.op.bias'''
         ]
         continue

      SCREAMING_SNAKE_CASE_:       Optional[Any]   	=renew_resnet_paths(lowercase       )
      SCREAMING_SNAKE_CASE_:       Dict   	={"""old""": f'''input_blocks.{i}.0''', """new""": f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
      SCREAMING_SNAKE_CASE_:       Optional[Any]   	={"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
      assign_to_checkpoint(
          lowercase							,	lowercase							,	lowercase							,	additional_replacements=[meta_path, resnet_op]							,	config=lowercase       )

      if len(lowercase       ):
         SCREAMING_SNAKE_CASE_:       Optional[Any]   	=renew_attention_paths(lowercase       )
         SCREAMING_SNAKE_CASE_:       Tuple   	={
             """old""": f'''input_blocks.{i}.1''',
             """new""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
         }
         SCREAMING_SNAKE_CASE_:       List[str]   	={
             f'''input_blocks.{i}.1.qkv.bias''': {
                 """key""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
                 """query""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
                 """value""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
             },
             f'''input_blocks.{i}.1.qkv.weight''': {
                 """key""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
                 """query""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
                 """value""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
             },
         }
         assign_to_checkpoint(
             lowercase							,	lowercase							,	lowercase							,	additional_replacements=[meta_path]							,	attention_paths_to_split=lowercase							,	config=lowercase							,	)

   SCREAMING_SNAKE_CASE_:       Dict   	=middle_blocks[0]
   SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=middle_blocks[1]
   SCREAMING_SNAKE_CASE_:       Dict   	=middle_blocks[2]

   SCREAMING_SNAKE_CASE_:       Any   	=renew_resnet_paths(lowercase       )
   assign_to_checkpoint(lowercase							,	lowercase							,	lowercase							,	config=lowercase       )

   SCREAMING_SNAKE_CASE_:       Dict   	=renew_resnet_paths(lowercase       )
   assign_to_checkpoint(lowercase							,	lowercase							,	lowercase							,	config=lowercase       )

   SCREAMING_SNAKE_CASE_:       Optional[int]   	=renew_attention_paths(lowercase       )
   SCREAMING_SNAKE_CASE_:       Optional[int]   	={
       """middle_block.1.qkv.bias""": {
           """key""": """mid_block.attentions.0.key.bias""",
           """query""": """mid_block.attentions.0.query.bias""",
           """value""": """mid_block.attentions.0.value.bias""",
       },
       """middle_block.1.qkv.weight""": {
           """key""": """mid_block.attentions.0.key.weight""",
           """query""": """mid_block.attentions.0.query.weight""",
           """value""": """mid_block.attentions.0.value.weight""",
       },
   }
   assign_to_checkpoint(
       lowercase							,	lowercase							,	lowercase							,	attention_paths_to_split=lowercase							,	config=lowercase       )

   for i in range(lowercase       ):
      SCREAMING_SNAKE_CASE_:       Tuple   	=i // (config["""num_res_blocks"""] + 1)
      SCREAMING_SNAKE_CASE_:       List[str]   	=i % (config["""num_res_blocks"""] + 1)
      SCREAMING_SNAKE_CASE_:       int   	=[shave_segments(lowercase							,	2       ) for name in output_blocks[i]]
      SCREAMING_SNAKE_CASE_:       Union[str, Any]   	={}

      for layer in output_block_layers:
         SCREAMING_SNAKE_CASE_:       Any   	=layer.split("""."""       )[0], shave_segments(lowercase							,	1       )
         if layer_id in output_block_list:
            output_block_list[layer_id].append(lowercase       )
         else:
            SCREAMING_SNAKE_CASE_:       str   	=[layer_name]

      if len(lowercase       ) > 1:
         SCREAMING_SNAKE_CASE_:       str   	=[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
         SCREAMING_SNAKE_CASE_:       Dict   	=[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

         SCREAMING_SNAKE_CASE_:       Optional[int]   	=renew_resnet_paths(lowercase       )
         SCREAMING_SNAKE_CASE_:       int   	=renew_resnet_paths(lowercase       )

         SCREAMING_SNAKE_CASE_:       Optional[int]   	={"""old""": f'''output_blocks.{i}.0''', """new""": f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
         assign_to_checkpoint(lowercase							,	lowercase							,	lowercase							,	additional_replacements=[meta_path]							,	config=lowercase       )

         if ["conv.weight", "conv.bias"] in output_block_list.values():
            SCREAMING_SNAKE_CASE_:       List[Any]   	=list(output_block_list.values()       ).index(["""conv.weight""", """conv.bias"""]       )
            SCREAMING_SNAKE_CASE_:       int   	=checkpoint[
                f'''output_blocks.{i}.{index}.conv.weight'''
            ]
            SCREAMING_SNAKE_CASE_:       int   	=checkpoint[
                f'''output_blocks.{i}.{index}.conv.bias'''
            ]

            # Clear attentions as they have been attributed above.
            if len(lowercase       ) == 2:
               SCREAMING_SNAKE_CASE_:       Tuple   	=[]

         if len(lowercase       ):
            SCREAMING_SNAKE_CASE_:       Dict   	=renew_attention_paths(lowercase       )
            SCREAMING_SNAKE_CASE_:       Tuple   	={
                """old""": f'''output_blocks.{i}.1''',
                """new""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
            }
            SCREAMING_SNAKE_CASE_:       Tuple   	={
                f'''output_blocks.{i}.1.qkv.bias''': {
                    """key""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
                    """query""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
                    """value""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
                },
                f'''output_blocks.{i}.1.qkv.weight''': {
                    """key""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
                    """query""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
                    """value""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
                },
            }
            assign_to_checkpoint(
                lowercase							,	lowercase							,	lowercase							,	additional_replacements=[meta_path]							,	attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions       ) else None							,	config=lowercase							,	)
      else:
         SCREAMING_SNAKE_CASE_:       int   	=renew_resnet_paths(lowercase							,	n_shave_prefix_segments=1       )
         for path in resnet_0_paths:
            SCREAMING_SNAKE_CASE_:       Tuple   	=""".""".join(["""output_blocks""", str(lowercase       ), path["""old"""]]       )
            SCREAMING_SNAKE_CASE_:       List[Any]   	=""".""".join(["""up_blocks""", str(lowercase       ), """resnets""", str(lowercase       ), path["""new"""]]       )

            SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=checkpoint[old_path]

   return new_checkpoint


if __name__ == "__main__":
   _UpperCAmelCase   =	argparse.ArgumentParser()

   parser.add_argument(
       """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
   )

   parser.add_argument(
       """--config_file""",
       default=None,
       type=str,
       required=True,
       help="""The config json file corresponding to the architecture.""",
   )

   parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

   _UpperCAmelCase   =	parser.parse_args()

   _UpperCAmelCase   =	torch.load(args.checkpoint_path)

   with open(args.config_file) as f:
      _UpperCAmelCase   =	json.loads(f.read())

   _UpperCAmelCase   =	convert_ldm_checkpoint(checkpoint, config)

   if "ldm" in config:
      del config["ldm"]

   _UpperCAmelCase   =	UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

   try:
      _UpperCAmelCase   =	DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
      _UpperCAmelCase   =	VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

      _UpperCAmelCase   =	LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
      pipe.save_pretrained(args.dump_path)
   except:  # noqa: E722
      model.save_pretrained(args.dump_path)








'''simple docstring'''





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 logging


_lowerCAmelCase      						=	logging.get_logger(__name__)
_lowerCAmelCase      						=	{'''vocab_file''': '''spiece.model'''}

_lowerCAmelCase      						=	{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

_lowerCAmelCase      						=	{
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

_lowerCAmelCase      						=	'''▁'''






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Any				=      VOCAB_FILES_NAMES
  __lowercase							:      Optional[Any]				=      PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:      int				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase=False  ,__UpperCAmelCase="[CLS]"  ,__UpperCAmelCase="[SEP]"  ,__UpperCAmelCase="<unk>"  ,__UpperCAmelCase="[SEP]"  ,__UpperCAmelCase="<pad>"  ,__UpperCAmelCase="[CLS]"  ,__UpperCAmelCase="[MASK]"  ,__UpperCAmelCase = None  ,**__UpperCAmelCase  ,)    ->		None:
      # Mask token behave like a normal word, i.e. include the space before it and
      # is included in the raw text, there should be a match in a non-normalized sentence.
      lowerCAmelCase__ :  Tuple							      =					(
          AddedToken(__UpperCAmelCase  ,lstrip=__UpperCAmelCase  ,rstrip=__UpperCAmelCase  ,normalized=__UpperCAmelCase      )
          if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      )
          else mask_token
      )

      lowerCAmelCase__ :  Any							      =					{} if sp_model_kwargs is None else sp_model_kwargs

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

      lowerCAmelCase__ :  str							      =					do_lower_case
      lowerCAmelCase__ :  int							      =					remove_space
      lowerCAmelCase__ :  Tuple							      =					keep_accents
      lowerCAmelCase__ :  Any							      =					vocab_file

      lowerCAmelCase__ :  Tuple							      =					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(__UpperCAmelCase      )




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      return len(self.sp_model      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Union[str, Any]							      =					{self.convert_ids_to_tokens(__UpperCAmelCase      ): i for i in range(self.vocab_size      )}
      vocab.update(self.added_tokens_encoder      )
      return vocab




  def __getstate__(    self      )    ->		Any:
      lowerCAmelCase__ :  Optional[Any]							      =					self.__dict__.copy()
      lowerCAmelCase__ :  Optional[Any]							      =					None
      return state




  def __setstate__(    self  ,__UpperCAmelCase      )    ->		List[Any]:
      lowerCAmelCase__ :  List[str]							      =					d

      # for backward compatibility
      if not hasattr(self  ,"""sp_model_kwargs"""      ):
          lowerCAmelCase__ :  Union[str, Any]							      =					{}

      lowerCAmelCase__ :  Any							      =					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(self.vocab_file      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[int]:
      if self.remove_space:
          lowerCAmelCase__ :  int							      =					""" """.join(inputs.strip().split()      )
      else:
          lowerCAmelCase__ :  str							      =					inputs
      lowerCAmelCase__ :  Tuple							      =					outputs.replace("""``"""  ,"""\""""      ).replace("""''"""  ,"""\""""      )

      if not self.keep_accents:
          lowerCAmelCase__ :  Any							      =					unicodedata.normalize("""NFKD"""  ,__UpperCAmelCase      )
          lowerCAmelCase__ :  Dict							      =					"""""".join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase      )]      )
      if self.do_lower_case:
          lowerCAmelCase__ :  Tuple							      =					outputs.lower()

      return outputs




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[str]:
      lowerCAmelCase__ :  List[str]							      =					self.preprocess_text(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[int]							      =					self.sp_model.encode(__UpperCAmelCase  ,out_type=__UpperCAmelCase      )
      lowerCAmelCase__ :  str							      =					[]
      for piece in pieces:
          if len(__UpperCAmelCase      ) > 1 and piece[-1] == str(""","""      ) and piece[-2].isdigit():
              lowerCAmelCase__ :  List[Any]							      =					self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase  ,""""""      )      )
              if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                  if len(cur_pieces[0]      ) == 1:
                      lowerCAmelCase__ :  str							      =					cur_pieces[1:]
                  else:
                      lowerCAmelCase__ :  int							      =					cur_pieces[0][1:]
              cur_pieces.append(piece[-1]      )
              new_pieces.extend(__UpperCAmelCase      )
          else:
              new_pieces.append(__UpperCAmelCase      )

      return new_pieces




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[str]:
      return self.sp_model.PieceToId(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:
      return self.sp_model.IdToPiece(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      lowerCAmelCase__ :  str							      =					[]
      lowerCAmelCase__ :  Tuple							      =					""""""
      lowerCAmelCase__ :  Tuple							      =					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(__UpperCAmelCase      ) + token
              lowerCAmelCase__ :  Union[str, Any]							      =					True
              lowerCAmelCase__ :  List[str]							      =					[]
          else:
              current_sub_tokens.append(__UpperCAmelCase      )
              lowerCAmelCase__ :  Optional[Any]							      =					False
      out_string += self.sp_model.decode(__UpperCAmelCase      )
      return out_string.strip()




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		List[int]:
      lowerCAmelCase__ :  int							      =					[self.sep_token_id]
      lowerCAmelCase__ :  Dict							      =					[self.cls_token_id]
      if token_ids_a is None:
          return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,__UpperCAmelCase = False      )    ->		List[int]:

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

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		List[int]:
      lowerCAmelCase__ :  List[str]							      =					[self.sep_token_id]
      lowerCAmelCase__ :  Tuple							      =					[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      ) * [0] + len(token_ids_a + sep      ) * [1]






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		Tuple[str]:
      if not os.path.isdir(__UpperCAmelCase      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      lowerCAmelCase__ :  int							      =					os.path.join(
          __UpperCAmelCase  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

      if os.path.abspath(self.vocab_file      ) != os.path.abspath(__UpperCAmelCase      ) and os.path.isfile(self.vocab_file      ):
          copyfile(self.vocab_file  ,__UpperCAmelCase      )
      elif not os.path.isfile(self.vocab_file      ):
          with open(__UpperCAmelCase  ,"""wb"""      ) as fi:
              lowerCAmelCase__ :  List[Any]							      =					self.sp_model.serialized_model_proto()
              fi.write(__UpperCAmelCase      )

      return (out_vocab_file,)


