



"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A:   Optional[int]								=		logging.get_logger(__name__)

A:   Union[str, Any]								=		{"vocab_file": "spiece.model"}

A:   Optional[Any]								=		{
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

A:   List[str]								=		{"bert_for_seq_generation": 5_1_2}



class       SCREAMING_SNAKE_CASE__  (					lowerCamelCase_ ):
   __lowerCAmelCase  :   Optional[Any]						=						VOCAB_FILES_NAMES
   __lowerCAmelCase  :   List[str]						=						PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase  :   List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase  :   Any						=						[]
   __lowerCAmelCase  :   Optional[Any]						=						['input_ids', 'attention_mask']

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE="<s>"      ,     _SCREAMING_SNAKE_CASE="</s>"      ,     _SCREAMING_SNAKE_CASE="<unk>"      ,     _SCREAMING_SNAKE_CASE="<pad>"      ,     _SCREAMING_SNAKE_CASE="<::::>"      ,     _SCREAMING_SNAKE_CASE = None      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	{} if sp_model_kwargs is None else sp_model_kwargs

    # Add extra_ids to the special token list
    super().__init__(
        bos_token=__SCREAMING_SNAKE_CASE      ,     eos_token=__SCREAMING_SNAKE_CASE      ,     unk_token=__SCREAMING_SNAKE_CASE      ,     pad_token=__SCREAMING_SNAKE_CASE      ,     sep_token=__SCREAMING_SNAKE_CASE      ,     sp_model_kwargs=self.sp_model_kwargs      ,     **__SCREAMING_SNAKE_CASE      ,     )

    UpperCAmelCase	:					Dict	=	vocab_file

    UpperCAmelCase	:					int	=	spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(__SCREAMING_SNAKE_CASE						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    return self.sp_model.get_piece_size()

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	{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						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	self.__dict__.copy()
    UpperCAmelCase	:					Union[str, Any]	=	None
    return state

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





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	d

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

    UpperCAmelCase	:					Tuple	=	spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(self.vocab_file						)

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





    '''simple docstring'''




    return self.sp_model.encode(__SCREAMING_SNAKE_CASE      ,     out_type=__SCREAMING_SNAKE_CASE						)

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





    '''simple docstring'''




    return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE						)
    return token

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





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	[]
    UpperCAmelCase	:					Any	=	''''''
    for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
      out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE						) + token
      UpperCAmelCase	:					List[str]	=	[]
     else:
      current_sub_tokens.append(__SCREAMING_SNAKE_CASE						)
    out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE						)
    return out_string.strip()




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      Any:





    '''simple docstring'''




    if not os.path.isdir(__SCREAMING_SNAKE_CASE						):
     logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
     return
    UpperCAmelCase	:					int	=	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	:					Any	=	self.sp_model.serialized_model_proto()
      fi.write(__SCREAMING_SNAKE_CASE						)

    return (out_vocab_file,)


'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase :  Union[str, Any]    							=       {
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  Dict    							=       ["Pix2StructImageProcessor"]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  List[str]    							=       [
							    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Pix2StructPreTrainedModel",
							    "Pix2StructForConditionalGeneration",
							    "Pix2StructVisionModel",
							    "Pix2StructTextModel",
							]

if TYPE_CHECKING:
							from .configuration_pixastruct import (
							    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    PixaStructConfig,
							    PixaStructTextConfig,
							    PixaStructVisionConfig,
							)
							from .processing_pixastruct import PixaStructProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_pixastruct import PixaStructImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pixastruct import (
														    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PixaStructForConditionalGeneration,
														    PixaStructPreTrainedModel,
														    PixaStructTextModel,
														    PixaStructVisionModel,
														)

else:
							import sys

							_lowercase :  Optional[int]    							=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



