




"""simple docstring"""






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_xlnet import XLNetTokenizer
else:
			lowercase__			=		None


lowercase__			=		logging.get_logger(__name__)

lowercase__			=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase__			=		{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

lowercase__			=		{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

lowercase__			=		"""▁"""

# Segments (not really needed)
lowercase__			=		0
lowercase__			=		1
lowercase__			=		2
lowercase__			=		3
lowercase__			=		4






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     VOCAB_FILES_NAMES
			lowerCamelCase__ =     PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__ =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__ =     """left"""
			lowerCamelCase__ =     XLNetTokenizer
			def __init__(    self    , lowercase=None    , lowercase=None    , lowercase=False    , lowercase=True    , lowercase=False    , lowercase="<s>"    , lowercase="</s>"    , lowercase="<unk>"    , lowercase="<sep>"    , lowercase="<pad>"    , lowercase="<cls>"    , lowercase="<mask>"    , lowercase=["<eop>", "<eod>"]    , **lowercase    , ):
							# Mask token behave like a normal word, i.e. include the space before it
							_lowerCamelCase	:     Optional[int]       =       AddedToken(lowercase    , lstrip=lowercase    , rstrip=lowercase    ) if isinstance(lowercase    , lowercase    ) else mask_token

							super().__init__(
							    vocab_file=lowercase    , tokenizer_file=lowercase    , do_lower_case=lowercase    , remove_space=lowercase    , keep_accents=lowercase    , bos_token=lowercase    , eos_token=lowercase    , unk_token=lowercase    , sep_token=lowercase    , pad_token=lowercase    , cls_token=lowercase    , mask_token=lowercase    , additional_special_tokens=lowercase    , **lowercase    , )

							_lowerCamelCase	:     Optional[int]       =       3
							_lowerCamelCase	:     List[str]       =       do_lower_case
							_lowerCamelCase	:     List[Any]       =       remove_space
							_lowerCamelCase	:     List[Any]       =       keep_accents
							_lowerCamelCase	:     List[str]       =       vocab_file
							_lowerCamelCase	:     Any       =       False if not self.vocab_file else True
			def    A_       (    self    , lowercase    , lowercase = None    ):
							_lowerCamelCase	:     Dict       =       [self.sep_token_id]
							_lowerCamelCase	:     Union[str, Any]       =       [self.cls_token_id]
							if token_ids_a is None:
											return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls
			def    A_       (    self    , lowercase    , lowercase = None    ):
							_lowerCamelCase	:     Any       =       [self.sep_token_id]
							_lowerCamelCase	:     List[str]       =       [2]

							if token_ids_a is None:
											return len(token_ids_a + sep    ) * [0] + cls_segment_id
							return len(token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1] + cls_segment_id






			def    A_       (    self    , lowercase    , lowercase = None    ):
							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(lowercase    ):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
											return
							_lowerCamelCase	:     Union[str, Any]       =       os.path.join(
							    lowercase    , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

							if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowercase    ):
											copyfile(self.vocab_file    , lowercase    )

							return (out_vocab_file,)





"""simple docstring"""






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 lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """layoutlmv3"""
			def __init__(    self    , lowercase=50265    , lowercase=768    , lowercase=12    , lowercase=12    , lowercase=3072    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=2    , lowercase=0.02    , lowercase=1E-5    , lowercase=1    , lowercase=0    , lowercase=2    , lowercase=1024    , lowercase=128    , lowercase=128    , lowercase=True    , lowercase=32    , lowercase=128    , lowercase=64    , lowercase=256    , lowercase=True    , lowercase=True    , lowercase=True    , lowercase=224    , lowercase=3    , lowercase=16    , lowercase=None    , **lowercase    , ):
							super().__init__(
							    vocab_size=lowercase    , hidden_size=lowercase    , num_hidden_layers=lowercase    , num_attention_heads=lowercase    , intermediate_size=lowercase    , hidden_act=lowercase    , hidden_dropout_prob=lowercase    , attention_probs_dropout_prob=lowercase    , max_position_embeddings=lowercase    , type_vocab_size=lowercase    , initializer_range=lowercase    , layer_norm_eps=lowercase    , pad_token_id=lowercase    , bos_token_id=lowercase    , eos_token_id=lowercase    , **lowercase    , )
							_lowerCamelCase	:     List[str]       =       max_ad_position_embeddings
							_lowerCamelCase	:     Optional[Any]       =       coordinate_size
							_lowerCamelCase	:     int       =       shape_size
							_lowerCamelCase	:     Optional[Any]       =       has_relative_attention_bias
							_lowerCamelCase	:     Dict       =       rel_pos_bins
							_lowerCamelCase	:     Tuple       =       max_rel_pos
							_lowerCamelCase	:     int       =       has_spatial_attention_bias
							_lowerCamelCase	:     Optional[int]       =       rel_ad_pos_bins
							_lowerCamelCase	:     List[Any]       =       max_rel_ad_pos
							_lowerCamelCase	:     Any       =       text_embed
							_lowerCamelCase	:     List[Any]       =       visual_embed
							_lowerCamelCase	:     str       =       input_size
							_lowerCamelCase	:     Optional[Any]       =       num_channels
							_lowerCamelCase	:     Tuple       =       patch_size
							_lowerCamelCase	:     Optional[int]       =       classifier_dropout






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     version.parse("""1.12"""		)
			@property
			def    A_       (    self    ):
							# 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    A_       (    self    ):
							return 1E-5
			@property
			def    A_       (    self    ):
							return 12






			def    A_       (    self    , lowercase    , lowercase = -1    , lowercase = -1    , lowercase = False    , lowercase = None    , lowercase = 3    , lowercase = 40    , lowercase = 40    , ):
							setattr(processor.image_processor    , 'apply_ocr'    , lowercase    )

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

							# Generate dummy bounding boxes
							_lowerCamelCase	:     List[str]       =       [[[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)
							_lowerCamelCase	:     Tuple       =       self._generate_dummy_images(lowercase    , lowercase    , lowercase    , lowercase    )

							_lowerCamelCase	:     str       =       dict(
							    processor(
							        lowercase    , text=lowercase    , boxes=lowercase    , return_tensors=lowercase    , )    )

							return inputs