






"""simple docstring"""



import os
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


__A		:       Union[str, Any]  				=		logging.get_logger(__name__)

__A		:       Tuple  				=		{'''vocab_file''': '''sentencepiece.bpe.model'''}

__A		:       Optional[Any]  				=		{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

__A		:       str  				=		{
    '''camembert-base''': 512,
}

__A		:       Any  				=		'''▁'''





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Any							    =   VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE_		:				str							    =   PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   ["input_ids", "attention_mask"]




					def __init__(				self     :						Any    ,						A     :						Any    ,						A     :						Optional[Any]="<s>"    ,						A     :						Optional[Any]="</s>"    ,						A     :						str="</s>"    ,						A     :						Optional[int]="<s>"    ,						A     :						List[str]="<unk>"    ,						A     :						List[Any]="<pad>"    ,						A     :						Optional[Any]="<mask>"    ,						A     :						Optional[Any]=["<s>NOTUSED", "</s>NOTUSED"]    ,						A     :						Optional[Dict[str, Any]] = None    ,						**A     :						List[Any]    ,						)  ->  None:
							# Mask token behave like a normal word, i.e. include the space before it
							lowercase_ : Union[str, Any]							       =   AddedToken(A    ,						lstrip=A    ,						rstrip=A   ) if isinstance(A    ,						A   ) else mask_token

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

							super().__init__(
							    bos_token=A    ,						eos_token=A    ,						unk_token=A    ,						sep_token=A    ,						cls_token=A    ,						pad_token=A    ,						mask_token=A    ,						additional_special_tokens=A    ,						sp_model_kwargs=self.sp_model_kwargs    ,						**A    ,						)
							lowercase_ : Tuple							       =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(A   )   )
							lowercase_ : Union[str, Any]							       =   vocab_file
							# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
							# sentencepiece vocabulary (this is the case for <s> and </s>
							lowercase_ : List[Any]							       =   {'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
							lowercase_ : Dict							       =   len(self.fairseq_tokens_to_ids   )
							lowercase_ : List[Any]							       =   len(self.sp_model   ) + len(self.fairseq_tokens_to_ids   )
							lowercase_ : int							       =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}




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

							if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase_ : List[str]							       =   [self.cls_token_id]
							lowercase_ : Union[str, Any]							       =   [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




					def   A  (				self     :						List[Any]    ,						A     :						List[int]    ,						A     :						Optional[List[int]] = None    ,						A     :						bool = 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, 1] + ([0] * len(A   )) + [1]




					def   A  (				self     :						Union[str, Any]    ,						A     :						List[int]    ,						A     :						Optional[List[int]] = None   )  ->  List[int]:
							lowercase_ : List[Any]							       =   [self.sep_token_id]
							lowercase_ : int							       =   [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]




					@property
					def   A  (				self     :						List[str]   )  ->  Union[str, Any]:
							return len(self.fairseq_tokens_to_ids   ) + len(self.sp_model   )




					def   A  (				self     :						Any   )  ->  Optional[Any]:
							lowercase_ : List[Any]							       =   {self.convert_ids_to_tokens(A   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab




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




					def   A  (				self     :						Union[str, Any]    ,						A     :						str   )  ->  Optional[Any]:
							if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
							elif self.sp_model.PieceToId(A   ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
							return self.fairseq_offset + self.sp_model.PieceToId(A   )




					def   A  (				self     :						int    ,						A     :						List[str]   )  ->  Dict:
							if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset   )




					def   A  (				self     :						Tuple    ,						A     :						Tuple   )  ->  Optional[Any]:
							lowercase_ : Tuple							       =   []
							lowercase_ : int							       =   ''''''
							lowercase_ : int							       =   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
											lowercase_ : Dict							       =   True
											lowercase_ : Any							       =   []
									else:
											current_sub_tokens.append(A   )
											lowercase_ : int							       =   False
							out_string += self.sp_model.decode(A   )
							return out_string.strip()




					def __getstate__(				self     :						Dict   )  ->  Union[str, Any]:
							lowercase_ : Optional[int]							       =   self.__dict__.copy()
							lowercase_ : Tuple							       =   None
							return state




					def __setstate__(				self     :						Optional[int]    ,						A     :						Optional[Any]   )  ->  Union[str, Any]:
							lowercase_ : Any							       =   d

							# for backward compatibility
							if not hasattr(self    ,						'''sp_model_kwargs'''   ):
									lowercase_ : List[str]							       =   {}

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






					def   A  (				self     :						List[Any]    ,						A     :						str    ,						A     :						Optional[str] = None   )  ->  Tuple[str]:
							if not os.path.isdir(A   ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
									return
							lowercase_ : Union[str, Any]							       =   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:
											lowercase_ : Union[str, Any]							       =   self.sp_model.serialized_model_proto()
											fi.write(A   )

							return (out_vocab_file,)










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


A    :					str =      logging.get_logger(__name__)

A    :					Union[str, Any] =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Optional[Any]							=    '''layoutlmv3'''






			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    Optional[int]=5_02_65							,	__lowerCAmelCase   :    Tuple=7_68							,	__lowerCAmelCase   :    Union[str, Any]=12							,	__lowerCAmelCase   :    Any=12							,	__lowerCAmelCase   :    List[Any]=30_72							,	__lowerCAmelCase   :    Optional[int]="gelu"							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[Any]=0.1							,	__lowerCAmelCase   :    Dict=5_12							,	__lowerCAmelCase   :    Any=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-5							,	__lowerCAmelCase   :    List[str]=1							,	__lowerCAmelCase   :    int=0							,	__lowerCAmelCase   :    Dict=2							,	__lowerCAmelCase   :    Tuple=10_24							,	__lowerCAmelCase   :    List[str]=1_28							,	__lowerCAmelCase   :    Optional[int]=1_28							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Optional[Any]=32							,	__lowerCAmelCase   :    Any=1_28							,	__lowerCAmelCase   :    str=64							,	__lowerCAmelCase   :    Optional[int]=2_56							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    int=2_24							,	__lowerCAmelCase   :    Dict=3							,	__lowerCAmelCase   :    List[Any]=16							,	__lowerCAmelCase   :    Dict=None							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	Dict:







					"""simple docstring"""






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


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    version.parse('''1.12'''    )






			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""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    a_       (			self   :    Optional[int]   )	->	float:







					"""simple docstring"""






					return 1e-5






			@property
			def    a_       (			self   :    Tuple   )	->	int:







					"""simple docstring"""






					return 12







			def    a_       (			self   :    str							,	__lowerCAmelCase   :    "ProcessorMixin"							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    bool = False							,	__lowerCAmelCase   :    Optional["TensorType"] = None							,	__lowerCAmelCase   :    int = 3							,	__lowerCAmelCase   :    int = 40							,	__lowerCAmelCase   :    int = 40							,	)	->	Mapping[str, Any]:







					"""simple docstring"""






					setattr(processor.image_processor							,	"""apply_ocr"""							,	__lowerCAmelCase   )

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

					# Generate dummy bounding boxes
					A__        =   [[[48, 84, 73, 1_28]]] * 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)
					A__        =   self._generate_dummy_images(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

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

					return inputs



