






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


SCREAMING_SNAKE_CASE__							=		logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE__							=		{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

SCREAMING_SNAKE_CASE__							=		{
    """moussaKam/mbarthez""": 10_24,
    """moussaKam/barthez""": 10_24,
    """moussaKam/barthez-orangesum-title""": 10_24,
}

SCREAMING_SNAKE_CASE__							=		"""▁"""
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  VOCAB_FILES_NAMES
				a__					       =  PRETRAINED_VOCAB_FILES_MAP
				a__					       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a__					       =  ['''input_ids''', '''attention_mask''']







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase="<s>"       ,					__lowerCAmelCase="</s>"       ,					__lowerCAmelCase="</s>"       ,					__lowerCAmelCase="<s>"       ,					__lowerCAmelCase="<unk>"       ,					__lowerCAmelCase="<pad>"       ,					__lowerCAmelCase="<mask>"       ,					__lowerCAmelCase = None       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      AddedToken(__lowerCAmelCase       ,					lstrip=__lowerCAmelCase       ,					rstrip=__lowerCAmelCase     ) if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) else mask_token

								__magic_name__		:Union[str, Any]        =      {} if sp_model_kwargs is None else sp_model_kwargs

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

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

								__magic_name__		:str        =      {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

								__magic_name__		:Dict        =      len(self.sp_model     ) - 1
								__magic_name__		:Optional[int]        =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""

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







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = False     ):
								"""simple docstring"""
								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__lowerCAmelCase       ,					token_ids_a=__lowerCAmelCase       ,					already_has_special_tokens=__lowerCAmelCase     )

								if token_ids_a is None:
												return [1] + ([0] * len(__lowerCAmelCase     )) + [1]
								return [1] + ([0] * len(__lowerCAmelCase     )) + [1, 1] + ([0] * len(__lowerCAmelCase     )) + [1]







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      [self.sep_token_id]
								__magic_name__		:Optional[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     ):
								"""simple docstring"""
								return len(self.sp_model     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      {self.convert_ids_to_tokens(__lowerCAmelCase     ): i for i in range(self.vocab_size     )}
								vocab.update(self.added_tokens_encoder     )
								return vocab







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return self.sp_model.encode(__lowerCAmelCase       ,					out_type=__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
								__magic_name__		:Optional[int]        =      self.sp_model.PieceToId(__lowerCAmelCase     )

								return spm_id if spm_id else self.unk_token_id







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								if index in self.fairseq_ids_to_tokens:
												return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      []
								__magic_name__		:Optional[Any]        =      ''''''
								__magic_name__		:Optional[Any]        =      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(__lowerCAmelCase     ) + token
																__magic_name__		:Any        =      True
																__magic_name__		:List[str]        =      []
												else:
																current_sub_tokens.append(__lowerCAmelCase     )
																__magic_name__		:List[str]        =      False
								out_string += self.sp_model.decode(__lowerCAmelCase     )
								return out_string.strip()







				def __getstate__(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      self.__dict__.copy()
								__magic_name__		:Union[str, Any]        =      None
								return state







				def __setstate__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      d

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

								__magic_name__		:str        =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(self.vocab_file     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								if not os.path.isdir(__lowerCAmelCase     ):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
												return
								__magic_name__		:int        =      os.path.join(
								    __lowerCAmelCase       ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

								if os.path.abspath(self.vocab_file     ) != os.path.abspath(__lowerCAmelCase     ) and os.path.isfile(self.vocab_file     ):
												copyfile(self.vocab_file       ,					__lowerCAmelCase     )
								elif not os.path.isfile(self.vocab_file     ):
												with open(__lowerCAmelCase       ,					'''wb'''     ) as fi:
																__magic_name__		:Optional[Any]        =      self.sp_model.serialized_model_proto()
																fi.write(__lowerCAmelCase     )

								return (out_vocab_file,)














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__      :			Tuple							=		{
    """configuration_table_transformer""": [
        """TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TableTransformerConfig""",
        """TableTransformerOnnxConfig""",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      :			List[str]							=		[
					    """TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TableTransformerForObjectDetection""",
					    """TableTransformerModel""",
					    """TableTransformerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_table_transformer import (
					    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    TableTransformerConfig,
					    TableTransformerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_table_transformer import (
										    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TableTransformerForObjectDetection,
										    TableTransformerModel,
										    TableTransformerPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






