






import os
import re
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__      :	Union[str, Any]       	=      logging.get_logger(__name__)

snake_case__      :	Optional[int]       	=      {"vocab_file": "spiece.model"}

snake_case__      :	int       	=      {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

snake_case__      :	List[str]       	=      {
    "google/bigbird-roberta-base": 4_0_9_6,
    "google/bigbird-roberta-large": 4_0_9_6,
    "google/bigbird-base-trivia-itc": 4_0_9_6,
}


class 						_a       (  _UpperCAmelCase    ):






  """simple docstring"""





  A_						=   VOCAB_FILES_NAMES
  A_						=   PRETRAINED_VOCAB_FILES_MAP
  A_						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_						=   ['''input_ids''', '''attention_mask''']
  A_						=   []




  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase="<unk>"		,		_UpperCAmelCase="<s>"		,		_UpperCAmelCase="</s>"		,		_UpperCAmelCase="<pad>"		,		_UpperCAmelCase="[SEP]"		,		_UpperCAmelCase="[MASK]"		,		_UpperCAmelCase="[CLS]"		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)			-> None:
     UpperCamelCase_									=     AddedToken(A_		,		lstrip=A_		,		rstrip=A_ ) if isinstance(A_		,		A_ ) else bos_token
     UpperCamelCase_									=     AddedToken(A_		,		lstrip=A_		,		rstrip=A_ ) if isinstance(A_		,		A_ ) else eos_token
     UpperCamelCase_									=     AddedToken(A_		,		lstrip=A_		,		rstrip=A_ ) if isinstance(A_		,		A_ ) else unk_token
     UpperCamelCase_									=     AddedToken(A_		,		lstrip=A_		,		rstrip=A_ ) if isinstance(A_		,		A_ ) else pad_token
     UpperCamelCase_									=     AddedToken(A_		,		lstrip=A_		,		rstrip=A_ ) if isinstance(A_		,		A_ ) else cls_token
     UpperCamelCase_									=     AddedToken(A_		,		lstrip=A_		,		rstrip=A_ ) if isinstance(A_		,		A_ ) else sep_token

     # Mask token behave like a normal word, i.e. include the space before it
     UpperCamelCase_									=     AddedToken(A_		,		lstrip=A_		,		rstrip=A_ ) if isinstance(A_		,		A_ ) else mask_token

     UpperCamelCase_									=     {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=A_		,		eos_token=A_		,		unk_token=A_		,		pad_token=A_		,		sep_token=A_		,		mask_token=A_		,		cls_token=A_		,		sp_model_kwargs=self.sp_model_kwargs		,		**A_		,		)

     UpperCamelCase_									=     vocab_file

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




  @property
  def    _UpperCAmelCase     (     self )			-> Tuple:
     return self.sp_model.get_piece_size()




  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_									=     {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 )			-> Union[str, Any]:
     UpperCamelCase_									=     self.__dict__.copy()
     UpperCamelCase_									=     None
     return state




  def __setstate__(     self		,		_UpperCAmelCase )			-> Optional[int]:
     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    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[str]:
     return self.sp_model.encode(A_		,		out_type=A_ )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Tuple:
     return self.sp_model.piece_to_id(A_ )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> str:
     UpperCamelCase_									=     self.sp_model.IdToPiece(A_ )
     return token




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> str:
     UpperCamelCase_									=     []
     UpperCamelCase_									=     ''
     UpperCamelCase_									=     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
           UpperCamelCase_									=     True
           UpperCamelCase_									=     []
        else:
           current_sub_tokens.append(A_ )
           UpperCamelCase_									=     False
     out_string += self.sp_model.decode(A_ )
     return out_string.strip()




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = False		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		**_UpperCAmelCase		,		)			-> str:
     UpperCamelCase_									=     kwargs.pop('use_source_tokenizer'		,		A_ )

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

     # 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(A_ ) )
              UpperCamelCase_									=     []
           sub_texts.append(A_ )
        else:
           current_sub_text.append(A_ )
     if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(A_ ) )

     # Mimic the behavior of the Rust tokenizer:
     # No space before [MASK] and [SEP]
     if spaces_between_special_tokens:
        UpperCamelCase_									=     re.sub(R' (\[(MASK|SEP)\])'		,		R'\1'		,		' '.join(A_ ) )
     else:
        UpperCamelCase_									=     ''.join(A_ )

     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(A_ )
        return clean_text
     else:
        return text




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> Tuple[str]:
     if not os.path.isdir(A_ ):
        logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
        return
     UpperCamelCase_									=     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:
           UpperCamelCase_									=     self.sp_model.serialized_model_proto()
           fi.write(A_ )

     return (out_vocab_file,)




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> List[int]:
     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     UpperCamelCase_									=     [self.cls_token_id]
     UpperCamelCase_									=     [self.sep_token_id]
     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=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] + ([0] * len(A_ )) + [1]




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> List[int]:
     UpperCamelCase_									=     [self.sep_token_id]
     UpperCamelCase_									=     [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]







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
 from ...processing_utils import ProcessorMixin
 from ...utils import TensorType


snake_case__      :	Tuple       	=      logging.get_logger(__name__)

snake_case__      :	str       	=      {
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """layoutlmv3"""




  def __init__(     self		,		_UpperCAmelCase=50265		,		_UpperCAmelCase=768		,		_UpperCAmelCase=12		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3072		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=512		,		_UpperCAmelCase=2		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-5		,		_UpperCAmelCase=1		,		_UpperCAmelCase=0		,		_UpperCAmelCase=2		,		_UpperCAmelCase=1024		,		_UpperCAmelCase=128		,		_UpperCAmelCase=128		,		_UpperCAmelCase=True		,		_UpperCAmelCase=32		,		_UpperCAmelCase=128		,		_UpperCAmelCase=64		,		_UpperCAmelCase=256		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=224		,		_UpperCAmelCase=3		,		_UpperCAmelCase=16		,		_UpperCAmelCase=None		,		**_UpperCAmelCase		,		)			-> Tuple:
     super().__init__(
         vocab_size=_UpperCAmelCase		,		hidden_size=_UpperCAmelCase		,		num_hidden_layers=_UpperCAmelCase		,		num_attention_heads=_UpperCAmelCase		,		intermediate_size=_UpperCAmelCase		,		hidden_act=_UpperCAmelCase		,		hidden_dropout_prob=_UpperCAmelCase		,		attention_probs_dropout_prob=_UpperCAmelCase		,		max_position_embeddings=_UpperCAmelCase		,		type_vocab_size=_UpperCAmelCase		,		initializer_range=_UpperCAmelCase		,		layer_norm_eps=_UpperCAmelCase		,		pad_token_id=_UpperCAmelCase		,		bos_token_id=_UpperCAmelCase		,		eos_token_id=_UpperCAmelCase		,		**_UpperCAmelCase		,		)
     UpperCamelCase_									=     max_ad_position_embeddings
     UpperCamelCase_									=     coordinate_size
     UpperCamelCase_									=     shape_size
     UpperCamelCase_									=     has_relative_attention_bias
     UpperCamelCase_									=     rel_pos_bins
     UpperCamelCase_									=     max_rel_pos
     UpperCamelCase_									=     has_spatial_attention_bias
     UpperCamelCase_									=     rel_ad_pos_bins
     UpperCamelCase_									=     max_rel_ad_pos
     UpperCamelCase_									=     text_embed
     UpperCamelCase_									=     visual_embed
     UpperCamelCase_									=     input_size
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     classifier_dropout




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   version.parse("""1.12"""    )




  @property
  def    _UpperCAmelCase     (     self )			-> Mapping[str, Mapping[int, str]]:
     # The order of inputs is different for question answering and sequence classification
     if self.task in ["question-answering", "sequence-classification"]:
        return OrderedDict(
            [
                ('input_ids', {0: 'batch', 1: 'sequence'}),
                ('attention_mask', {0: 'batch', 1: 'sequence'}),
                ('bbox', {0: 'batch', 1: 'sequence'}),
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ] )
     else:
        return OrderedDict(
            [
                ('input_ids', {0: 'batch', 1: 'sequence'}),
                ('bbox', {0: 'batch', 1: 'sequence'}),
                ('attention_mask', {0: 'batch', 1: 'sequence'}),
                ('pixel_values', {0: 'batch', 1: 'num_channels'}),
            ] )




  @property
  def    _UpperCAmelCase     (     self )			-> float:
     return 1e-5




  @property
  def    _UpperCAmelCase     (     self )			-> int:
     return 12




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = -1		,		_UpperCAmelCase = -1		,		_UpperCAmelCase = False		,		_UpperCAmelCase = None		,		_UpperCAmelCase = 3		,		_UpperCAmelCase = 40		,		_UpperCAmelCase = 40		,		)			-> Mapping[str, Any]:
     setattr(processor.image_processor		,		'apply_ocr'		,		_UpperCAmelCase )

     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     UpperCamelCase_									=     compute_effective_axis_dimension(
         _UpperCAmelCase		,		fixed_dimension=OnnxConfig.default_fixed_batch		,		num_token_to_add=0 )
     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     UpperCamelCase_									=     processor.tokenizer.num_special_tokens_to_add(_UpperCAmelCase )
     UpperCamelCase_									=     compute_effective_axis_dimension(
         _UpperCAmelCase		,		fixed_dimension=OnnxConfig.default_fixed_sequence		,		num_token_to_add=_UpperCAmelCase )
     # Generate dummy inputs according to compute batch and sequence
     UpperCamelCase_									=     [[' '.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size

     # Generate dummy bounding boxes
     UpperCamelCase_									=     [[[48, 84, 73, 128]]] * batch_size

     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
     UpperCamelCase_									=     self._generate_dummy_images(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )

     UpperCamelCase_									=     dict(
         processor(
             _UpperCAmelCase		,		text=_UpperCAmelCase		,		boxes=_UpperCAmelCase		,		return_tensors=_UpperCAmelCase		,		) )

     return inputs





