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


snake_case__				= logging.get_logger(__name__)
snake_case__				= {"vocab_file": "spiece.model"}

snake_case__				= {
    "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",
    }
}

snake_case__				= {
    "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,
}

snake_case__				= "▁"



class  UpperCamelCase				( _lowerCamelCase ):

     '''simple docstring'''

     A_										=  VOCAB_FILES_NAMES
     A_										=  PRETRAINED_VOCAB_FILES_MAP
     A_										=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



     def __init__(					self				,				A_				,				A_=True				,				A_=True				,				A_=False				,				A_="[CLS]"				,				A_="[SEP]"				,				A_="<unk>"				,				A_="[SEP]"				,				A_="<pad>"				,				A_="[CLS]"				,				A_="[MASK]"				,				A_ = None				,				**A_				,				) ->				None:






           """simple docstring"""
           # 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			    =				(
               AddedToken(A__				,				lstrip=A__				,				rstrip=A__				,				normalized=A__						)
               if isinstance(A__				,				A__						)
               else mask_token
           )

           _lowerCamelCase			    =				{} if sp_model_kwargs is None else sp_model_kwargs

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

           _lowerCamelCase			    =				do_lower_case
           _lowerCamelCase			    =				remove_space
           _lowerCamelCase			    =				keep_accents
           _lowerCamelCase			    =				vocab_file

           _lowerCamelCase			    =				spm.SentencePieceProcessor(**self.sp_model_kwargs						)
           self.sp_model.Load(A__						)



     @property
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return len(self.sp_model						)



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				{self.convert_ids_to_tokens(A__						): i for i in range(self.vocab_size						)}
           vocab.update(self.added_tokens_encoder						)
           return vocab



     def __getstate__(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.__dict__.copy()
           _lowerCamelCase			    =				None
           return state



     def __setstate__(					self				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				d

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

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



     def 		UpperCamelCase_						(					self				,				A_						) ->				Union[str, Any]:






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

           if not self.keep_accents:
                 _lowerCamelCase			    =				unicodedata.normalize('''NFKD'''				,				A__						)
                 _lowerCamelCase			    =				''''''.join([c for c in outputs if not unicodedata.combining(A__						)]						)
           if self.do_lower_case:
                 _lowerCamelCase			    =				outputs.lower()

           return outputs



     def 		UpperCamelCase_						(					self				,				A_						) ->				List[str]:






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

           return new_pieces



     def 		UpperCamelCase_						(					self				,				A_						) ->				Tuple:






           """simple docstring"""
           return self.sp_model.PieceToId(A__						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				List[str]:






           """simple docstring"""
           return self.sp_model.IdToPiece(A__						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				''''''
           _lowerCamelCase			    =				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
                       _lowerCamelCase			    =				True
                       _lowerCamelCase			    =				[]
                 else:
                       current_sub_tokens.append(A__						)
                       _lowerCamelCase			    =				False
           out_string += self.sp_model.decode(A__						)
           return out_string.strip()



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				List[int]:






           """simple docstring"""
           _lowerCamelCase			    =				[self.sep_token_id]
           _lowerCamelCase			    =				[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				,				A_				,				A_ = None				,				A_ = 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 not None:
                 return [1] + ([0] * len(A__						)) + [1] + ([0] * len(A__						)) + [1]
           return [1] + ([0] * len(A__						)) + [1]



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				List[int]:






           """simple docstring"""
           _lowerCamelCase			    =				[self.sep_token_id]
           _lowerCamelCase			    =				[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				,				A_				,				A_ = None						) ->				Tuple[str]:






           """simple docstring"""
           if not os.path.isdir(A__						):
                 logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
                 return
           _lowerCamelCase			    =				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:
                       _lowerCamelCase			    =				self.sp_model.serialized_model_proto()
                       fi.write(A__						)

           return (out_vocab_file,)
from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip