
'''simple docstring'''





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   "▁"

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

UpperCAmelCase    					=   {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

UpperCAmelCase    					=   {
    "facebook/xglm-564M": 2_048,
}

class 						a   (				__magic_name__    ):
				_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	:				Tuple,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]="<s>",    SCREAMING_SNAKE_CASE_	:				List[Any]="</s>",    SCREAMING_SNAKE_CASE_	:				List[str]="</s>",    SCREAMING_SNAKE_CASE_	:				Any="<s>",    SCREAMING_SNAKE_CASE_	:				Optional[Any]="<unk>",    SCREAMING_SNAKE_CASE_	:				Union[str, Any]="<pad>",    SCREAMING_SNAKE_CASE_	:				Optional[Dict[str, Any]] = None,    **SCREAMING_SNAKE_CASE_	:				str,    ):
										snake_case       :     List[Any] =    {} if sp_model_kwargs is None else sp_model_kwargs

										# Compatibility with the original tokenizer
										snake_case       :     Dict =    7
										snake_case       :     List[Any] =    [F"""<madeupword{i}>""" for i in range(self.num_madeup_words      )]

										snake_case       :     str =    kwargs.get('''additional_special_tokens''',    []      )
										kwargs["additional_special_tokens"] += [
										    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
										]

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

										snake_case       :     Optional[Any] =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.Load(str(SCREAMING_SNAKE_CASE_      )      )
										snake_case       :     List[Any] =    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'

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

										# Mimic fairseq token-to-id alignment for the first 4 token
										snake_case       :     List[str] =    {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

										snake_case       :     List[str] =    len(self.sp_model      )
										snake_case       :     Optional[int] =    {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words      )}
										self.fairseq_tokens_to_ids.update(SCREAMING_SNAKE_CASE_      )

										snake_case       :     Tuple =    {v: k for k, v in self.fairseq_tokens_to_ids.items()}




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




				def __setstate__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										snake_case       :     Union[str, Any] =    d

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

										snake_case       :     Any =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				List[int],    SCREAMING_SNAKE_CASE_	:				Optional[List[int]] = None      ):

										if token_ids_a is None:
																return [self.sep_token_id] + token_ids_a
										snake_case       :     Optional[Any] =    [self.sep_token_id]
										return sep + token_ids_a + sep + sep + token_ids_a




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				List[int],    SCREAMING_SNAKE_CASE_	:				Optional[List[int]] = None,    SCREAMING_SNAKE_CASE_	:				bool = False      ):

										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=SCREAMING_SNAKE_CASE_,    token_ids_a=SCREAMING_SNAKE_CASE_,    already_has_special_tokens=SCREAMING_SNAKE_CASE_      )

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




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				List[int],    SCREAMING_SNAKE_CASE_	:				Optional[List[int]] = None      ):
										snake_case       :     Any =    [self.sep_token_id]

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




				@property
				def 	__snake_case							(							self	:				List[Any]      ):
										return len(self.sp_model      ) + self.fairseq_offset + self.num_madeup_words




				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     Any =    {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_      ): i for i in range(self.vocab_size      )}
										vocab.update(self.added_tokens_encoder      )
										return vocab




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				str      ):
										return self.sp_model.encode(SCREAMING_SNAKE_CASE_,    out_type=SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										snake_case       :     int =    self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_      )

										# 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 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				int      ):
										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	:				str,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										snake_case       :     int =    ''''''.join(SCREAMING_SNAKE_CASE_      ).replace(SCREAMING_SNAKE_CASE_,    ''' '''      ).strip()
										return out_string




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										if not os.path.isdir(SCREAMING_SNAKE_CASE_      ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
																return
										snake_case       :     List[Any] =    os.path.join(
										    SCREAMING_SNAKE_CASE_,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

										if os.path.abspath(self.vocab_file      ) != os.path.abspath(SCREAMING_SNAKE_CASE_      ) and os.path.isfile(self.vocab_file      ):
																copyfile(self.vocab_file,    SCREAMING_SNAKE_CASE_      )
										elif not os.path.isfile(self.vocab_file      ):
																with open(SCREAMING_SNAKE_CASE_,    '''wb'''      ) as fi:
																						snake_case       :     List[str] =    self.sp_model.serialized_model_proto()
																						fi.write(SCREAMING_SNAKE_CASE_      )

										return (out_vocab_file,)



'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


UpperCAmelCase    					=   logging.get_logger(__name__)

class 						a   (				__magic_name__    ):




				def __init__(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
										warnings.warn(
										    '''The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
										    ''' Please use LayoutLMv2ImageProcessor instead.''',    SCREAMING_SNAKE_CASE_,    )
										super().__init__(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )

