





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase       		=      {
    """configuration_layoutlmv3""": [
        """LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LayoutLMv3Config""",
        """LayoutLMv3OnnxConfig""",
    ],
    """processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
    """tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase       		=      ["""LayoutLMv3TokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase       		=      [
					    """LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LayoutLMv3ForQuestionAnswering""",
					    """LayoutLMv3ForSequenceClassification""",
					    """LayoutLMv3ForTokenClassification""",
					    """LayoutLMv3Model""",
					    """LayoutLMv3PreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase       		=      [
					    """TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFLayoutLMv3ForQuestionAnswering""",
					    """TFLayoutLMv3ForSequenceClassification""",
					    """TFLayoutLMv3ForTokenClassification""",
					    """TFLayoutLMv3Model""",
					    """TFLayoutLMv3PreTrainedModel""",
					]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase       		=      ["""LayoutLMv3FeatureExtractor"""]
					lowerCAmelCase       		=      ["""LayoutLMv3ImageProcessor"""]


if TYPE_CHECKING:
					from .configuration_layoutlmva import (
					    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    LayoutLMvaConfig,
					    LayoutLMvaOnnxConfig,
					)
					from .processing_layoutlmva import LayoutLMvaProcessor
					from .tokenization_layoutlmva import LayoutLMvaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlmva import (
										    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMvaForQuestionAnswering,
										    LayoutLMvaForSequenceClassification,
										    LayoutLMvaForTokenClassification,
										    LayoutLMvaModel,
										    LayoutLMvaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_layoutlmva import (
										    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFLayoutLMvaForQuestionAnswering,
										    TFLayoutLMvaForSequenceClassification,
										    TFLayoutLMvaForTokenClassification,
										    TFLayoutLMvaModel,
										    TFLayoutLMvaPreTrainedModel,
										)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
										from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
					import sys

					lowerCAmelCase       		=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










'''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


lowerCAmelCase       		=      logging.get_logger(__name__)

lowerCAmelCase       		=      """▁"""

lowerCAmelCase       		=      {"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCAmelCase       		=      {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowerCAmelCase       		=      {
    """xlm-roberta-base""": 5_12,
    """xlm-roberta-large""": 5_12,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_12,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_12,
    """xlm-roberta-large-finetuned-conll03-english""": 5_12,
    """xlm-roberta-large-finetuned-conll03-german""": 5_12,
}
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   VOCAB_FILES_NAMES
	snake_case_      =   PRETRAINED_VOCAB_FILES_MAP
	snake_case_      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_      =   ["input_ids", "attention_mask"]



	def __init__(   self		,      a_		,      a_="<s>"		,      a_="</s>"		,      a_="</s>"		,      a_="<s>"		,      a_="<unk>"		,      a_="<pad>"		,      a_="<mask>"		,      a_ = None		,      **a_		,      ):
			# Mask token behave like a normal word, i.e. include the space before it
			lowerCAmelCase     :    str						=   AddedToken(a_		,      lstrip=a_		,      rstrip=a_	) if isinstance(a_		,      a_	) else mask_token

			lowerCAmelCase     :    Tuple						=   {} 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_		,      sp_model_kwargs=self.sp_model_kwargs		,      **a_		,      )

			lowerCAmelCase     :    Optional[int]						=   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
			self.sp_model.Load(str(a_	)	)
			lowerCAmelCase     :    int						=   vocab_file

			# Original fairseq vocab and spm vocab must be "aligned":
			# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
			# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
			# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
			# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

			# Mimic fairseq token-to-id alignment for the first 4 token
			lowerCAmelCase     :    Any						=   {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

			# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
			lowerCAmelCase     :    Dict						=   1

			lowerCAmelCase     :    Optional[Any]						=   len(self.sp_model	) + self.fairseq_offset
			lowerCAmelCase     :    List[Any]						=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}



	def __getstate__(   self	):
			lowerCAmelCase     :    Any						=   self.__dict__.copy()
			lowerCAmelCase     :    Optional[int]						=   None
			lowerCAmelCase     :    List[str]						=   self.sp_model.serialized_model_proto()
			return state



	def __setstate__(   self		,      a_	):
			lowerCAmelCase     :    Any						=   d

			# for backward compatibility
			if not hasattr(self		,      "sp_model_kwargs"	):
					lowerCAmelCase     :    Any						=   {}

			lowerCAmelCase     :    Optional[Any]						=   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)



	def   _lowerCamelCase    (   self		,      a_		,      a_ = None	):

			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			lowerCAmelCase     :    Optional[Any]						=   [self.cls_token_id]
			lowerCAmelCase     :    List[Any]						=   [self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep



	def   _lowerCamelCase    (   self		,      a_		,      a_ = None		,      a_ = False	):

			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   _lowerCamelCase    (   self		,      a_		,      a_ = None	):
			lowerCAmelCase     :    Dict						=   [self.sep_token_id]
			lowerCAmelCase     :    List[Any]						=   [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   _lowerCamelCase    (   self	):
			return len(self.sp_model	) + self.fairseq_offset + 1  # Add the <mask> token



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   {self.convert_ids_to_tokens(a_	): i for i in range(self.vocab_size	)}
			vocab.update(self.added_tokens_encoder	)
			return vocab



	def   _lowerCamelCase    (   self		,      a_	):
			return self.sp_model.encode(a_		,      out_type=a_	)



	def   _lowerCamelCase    (   self		,      a_	):
			if token in self.fairseq_tokens_to_ids:
					return self.fairseq_tokens_to_ids[token]
			lowerCAmelCase     :    int						=   self.sp_model.PieceToId(a_	)

			# Need to return unknown token if the SP model returned 0
			return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



	def   _lowerCamelCase    (   self		,      a_	):
			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   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    Dict						=   "".join(a_	).replace(a_		,      " "	).strip()
			return out_string





	def   _lowerCamelCase    (   self		,      a_		,      a_ = None	):
			if not os.path.isdir(a_	):
					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
					return
			lowerCAmelCase     :    int						=   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:
							lowerCAmelCase     :    Dict						=   self.sp_model.serialized_model_proto()
							fi.write(a_	)

			return (out_vocab_file,)



