


'''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		=logging.get_logger(__name__)

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

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

__A		={
    'camembert-base': 5_12,
}

__A		='▁'


class 						_snake_case   (       a__		):
					lowerCAmelCase :str						=   VOCAB_FILES_NAMES
					lowerCAmelCase :Optional[Any]						=   PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase :Any						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase :str						=   ['''input_ids''', '''attention_mask''']




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

								UpperCAmelCase__					:   Dict       				=       {} 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							,							additional_special_tokens=_lowerCamelCase							,							sp_model_kwargs=self.sp_model_kwargs							,							**_lowerCamelCase							,							)
								UpperCAmelCase__					:   Optional[int]       				=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(str(_lowerCamelCase))
								UpperCAmelCase__					:   Optional[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>
								UpperCAmelCase__					:   str       				=       {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
								UpperCAmelCase__					:   Tuple       				=       len(self.fairseq_tokens_to_ids)
								UpperCAmelCase__					:   Any       				=       len(self.sp_model) + len(self.fairseq_tokens_to_ids)
								UpperCAmelCase__					:   List[Any]       				=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}




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

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase__					:   str       				=       [self.cls_token_id]
								UpperCAmelCase__					:   Dict       				=       [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = False):
								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 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__					:   Tuple       				=       [self.sep_token_id]
								UpperCAmelCase__					:   Union[str, 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 			snake_case__  ( self):
								return len(self.fairseq_tokens_to_ids) + len(self.sp_model)




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




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




					def 			snake_case__  ( self							,							_lowerCamelCase):
								if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(_lowerCamelCase) == 0:
											# Convert sentence piece unk token to fairseq unk token index
											return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								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 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   List[str]       				=       []
								UpperCAmelCase__					:   Optional[Any]       				=       """"""
								UpperCAmelCase__					:   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
														UpperCAmelCase__					:   List[Any]       				=       True
														UpperCAmelCase__					:   Tuple       				=       []
											else:
														current_sub_tokens.append(_lowerCamelCase)
														UpperCAmelCase__					:   Union[str, Any]       				=       False
								out_string += self.sp_model.decode(_lowerCamelCase)
								return out_string.strip()




					def __getstate__( self):
								UpperCAmelCase__					:   Optional[Any]       				=       self.__dict__.copy()
								UpperCAmelCase__					:   List[str]       				=       None
								return state




					def __setstate__( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   List[str]       				=       d

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

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




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if not os.path.isdir(_lowerCamelCase):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
											return
								UpperCAmelCase__					:   Any       				=       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:
														UpperCAmelCase__					:   Union[str, Any]       				=       self.sp_model.serialized_model_proto()
														fi.write(_lowerCamelCase)

								return (out_vocab_file,)



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A		={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    '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

		__A		=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)