





'''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


a__							:							List[str] =logging.get_logger(__name__)

a__							:							Any ='''▁'''

a__							:							Dict ={'''vocab_file''': '''sentencepiece.bpe.model'''}

a__							:							List[Any] ={
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

a__							:							List[Any] ={
    '''facebook/xglm-564M''': 2_048,
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[Any]					=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_	:					List[Any]					=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_	:					Dict					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=["input_ids", "attention_mask"]






 def __init__(       self       :							Dict							,	__A       :							List[Any]							,	__A       :							Dict="<s>"							,	__A       :							Any="</s>"							,	__A       :							Union[str, Any]="</s>"							,	__A       :							Optional[int]="<s>"							,	__A       :							Dict="<unk>"							,	__A       :							Optional[Any]="<pad>"							,	__A       :							Optional[Dict[str, Any]] = None							,	**__A       :							Union[str, Any]							,	):
        __UpperCamelCase       =  {} if sp_model_kwargs is None else sp_model_kwargs

        # Compatibility with the original tokenizer
        __UpperCamelCase       =  7
        __UpperCamelCase       =  [f'''<madeupword{i}>''' for i in range(self.num_madeup_words	)]

        __UpperCamelCase       =  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=__A							,	eos_token=__A							,	unk_token=__A							,	sep_token=__A							,	cls_token=__A							,	pad_token=__A							,	sp_model_kwargs=self.sp_model_kwargs							,	**__A							,	)

        __UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(str(__A	)	)
        __UpperCamelCase       =  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
        __UpperCamelCase       =  1

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

        __UpperCamelCase       =  len(self.sp_model	)
        __UpperCamelCase       =  {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words	)}
        self.fairseq_tokens_to_ids.update(__A	)

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






 def __getstate__(       self       :							Tuple	):
        __UpperCamelCase       =  self.__dict__.copy()
        __UpperCamelCase       =  None
        __UpperCamelCase       =  self.sp_model.serialized_model_proto()
        return state






 def __setstate__(       self       :							Any							,	__A       :							Optional[int]	):
        __UpperCamelCase       =  d

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

        __UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None	):

        if token_ids_a is None:
               return [self.sep_token_id] + token_ids_a
        __UpperCamelCase       =  [self.sep_token_id]
        return sep + token_ids_a + sep + sep + token_ids_a






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None							,	__A       :							bool = 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	))
        return [1] + ([0] * len(__A	)) + [1, 1] + ([0] * len(__A	))






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None	):
        __UpperCamelCase       =  [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        _lowerCamelCase					(       self       :							Optional[int]	):
        return len(self.sp_model	) + self.fairseq_offset + self.num_madeup_words






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  {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       :							List[Any]							,	__A       :							str	):
        return self.sp_model.encode(__A							,	out_type=__A	)






 def        _lowerCamelCase					(       self       :							int							,	__A       :							Any	):
        if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
        __UpperCamelCase       =  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       :							List[str]							,	__A       :							List[str]	):
        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       :							Dict							,	__A       :							int	):
        __UpperCamelCase       =  ''.join(__A	).replace(__A							,	' '	).strip()
        return out_string






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

        return (out_vocab_file,)




import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


_snake_case             =     logging.get_logger(__name__)
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				*__A						,				**__A ):






     """simple docstring"""





     warnings.warn(
         "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
         " use DPTImageProcessor instead."						,				__A						,				)
     super().__init__(*__A						,				**__A )



