





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


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}
class     SCREAMING_SNAKE_CASE       (SCREAMING_SNAKE_CASE__				):
   _UpperCamelCase   :		str			 =	'''layoutlmv3'''


   def __init__(   self      :    Optional[Any]      ,					a      :    List[str]=50_265      ,					a      :    Optional[Any]=768      ,					a      :    str=12      ,					a      :    str=12      ,					a      :    List[Any]=3_072      ,					a      :    List[str]="gelu"      ,					a      :    Any=0.1      ,					a      :    int=0.1      ,					a      :    Tuple=512      ,					a      :    Any=2      ,					a      :    Dict=0.02      ,					a      :    List[str]=1E-5      ,					a      :    Dict=1      ,					a      :    List[str]=0      ,					a      :    str=2      ,					a      :    Optional[int]=1_024      ,					a      :    Optional[int]=128      ,					a      :    List[str]=128      ,					a      :    int=True      ,					a      :    Optional[Any]=32      ,					a      :    List[str]=128      ,					a      :    List[Any]=64      ,					a      :    Tuple=256      ,					a      :    Dict=True      ,					a      :    Optional[Any]=True      ,					a      :    Union[str, Any]=True      ,					a      :    Tuple=224      ,					a      :    str=3      ,					a      :    Union[str, Any]=16      ,					a      :    Optional[int]=None      ,					**a      :    Optional[Any]      ,					)->       Dict:





       """simple docstring"""







       super().__init__(
           vocab_size=A__      ,					hidden_size=A__      ,					num_hidden_layers=A__      ,					num_attention_heads=A__      ,					intermediate_size=A__      ,					hidden_act=A__      ,					hidden_dropout_prob=A__      ,					attention_probs_dropout_prob=A__      ,					max_position_embeddings=A__      ,					type_vocab_size=A__      ,					initializer_range=A__      ,					layer_norm_eps=A__      ,					pad_token_id=A__      ,					bos_token_id=A__      ,					eos_token_id=A__      ,					**A__      ,					)
       lowercase__           =  max_ad_position_embeddings
       lowercase__           =  coordinate_size
       lowercase__           =  shape_size
       lowercase__           =  has_relative_attention_bias
       lowercase__           =  rel_pos_bins
       lowercase__           =  max_rel_pos
       lowercase__           =  has_spatial_attention_bias
       lowercase__           =  rel_ad_pos_bins
       lowercase__           =  max_rel_ad_pos
       lowercase__           =  text_embed
       lowercase__           =  visual_embed
       lowercase__           =  input_size
       lowercase__           =  num_channels
       lowercase__           =  patch_size
       lowercase__           =  classifier_dropout






class     SCREAMING_SNAKE_CASE       (SCREAMING_SNAKE_CASE__				):
   _UpperCamelCase   :		List[Any]			 =	version.parse('1.12'				)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Dict:





       """simple docstring"""







       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 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       List[str]:





       """simple docstring"""







       return 1E-5


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       Tuple:





       """simple docstring"""







       return 12


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    List[Any]      ,					a      :    Union[str, Any] = -1      ,					a      :    Dict = -1      ,					a      :    Dict = False      ,					a      :    Union[str, Any] = None      ,					a      :    Optional[Any] = 3      ,					a      :    List[Any] = 40      ,					a      :    Any = 40      ,					)->       Optional[int]:





       """simple docstring"""







       setattr(processor.image_processor      ,					'apply_ocr'      ,					A__	)

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

       # Generate dummy bounding boxes
       lowercase__           =  [[[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)
       lowercase__           =  self._generate_dummy_images(A__      ,					A__      ,					A__      ,					A__	)

       lowercase__           =  dict(
           processor(
               A__      ,					text=A__      ,					boxes=A__      ,					return_tensors=A__      ,					)	)

       return inputs









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_camembert import CamembertTokenizer
else:
      lowercase_          =		None


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase_          =		{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

lowercase_          =		{
    """camembert-base""": 512,
}

lowercase_          =		"""▁"""
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		int			 =	VOCAB_FILES_NAMES
   _UpperCamelCase   :		Dict			 =	PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase   :		Dict			 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase   :		Dict			 =	['input_ids', 'attention_mask']
   _UpperCamelCase   :		Optional[int]			 =	CamembertTokenizer


   def __init__(   self      :    List[Any]      ,					a      :    Union[str, Any]=None      ,					a      :    str=None      ,					a      :    Tuple="<s>"      ,					a      :    Tuple="</s>"      ,					a      :    Optional[int]="</s>"      ,					a      :    int="<s>"      ,					a      :    Dict="<unk>"      ,					a      :    Optional[Any]="<pad>"      ,					a      :    Union[str, Any]="<mask>"      ,					a      :    Any=["<s>NOTUSED", "</s>NOTUSED"]      ,					**a      :    int      ,					)->       str:





     """simple docstring"""







     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else mask_token

     super().__init__(
         a      ,					tokenizer_file=a      ,					bos_token=a      ,					eos_token=a      ,					sep_token=a      ,					cls_token=a      ,					unk_token=a      ,					pad_token=a      ,					mask_token=a      ,					additional_special_tokens=a      ,					**a      ,					)

     lowercase__           =  vocab_file
     lowercase__           =  False if not self.vocab_file else True


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					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]
     lowercase__           =  [self.cls_token_id]
     lowercase__           =  [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None	)->       List[int]:





     """simple docstring"""







     lowercase__           =  [self.sep_token_id]
     lowercase__           =  [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]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    str      ,					a      :    Optional[str] = None	)->       Tuple[str]:





     """simple docstring"""







     if not self.can_save_slow_tokenizer:
       raise ValueError(
           'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
           'tokenizer.'	)

     if not os.path.isdir(a	):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
       return
     lowercase__           =  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	):
       copyfile(self.vocab_file      ,					a	)

     return (out_vocab_file,)


