



'''simple docstring'''

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

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


a							   =				logging.get_logger(__name__)

a							   =				'▁'

a							   =				{'vocab_file': 'prophetnet.tokenizer'}

a							   =				{
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

a							   =				{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

a							   =				{
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    int      							=collections.OrderedDict()
			with open(__lowerCAmelCase				,							'r'				,							encoding='utf-8'					) as reader:
						snake_case:    Optional[Any]      							=reader.readlines()
			for index, token in enumerate(__lowerCAmelCase					):
						snake_case:    Any      							=token.rstrip('\n'					)
						snake_case:    Optional[Any]      							=index
			return vocab







class 						a_		(     UpperCAmelCase_    ):
							UpperCAmelCase			:		Union[str, Any]        =				VOCAB_FILES_NAMES
							UpperCAmelCase			:		Union[str, Any]        =				PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase			:		str        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase			:		Optional[int]        =				['''input_ids''', '''attention_mask''']


							def __init__(				self   :						List[str]      ,						a_   :						Optional[Any]      ,						a_   :						str="[SEP]"      ,						a_   :						Tuple="[SEP]"      ,						a_   :						Dict="[SEP]"      ,						a_   :						Union[str, Any]="[UNK]"      ,						a_   :						Any="[PAD]"      ,						a_   :						List[str]="[CLS]"      ,						a_   :						Union[str, Any]="[MASK]"      ,						a_   :						Optional[Dict[str, Any]] = None      ,						**a_   :						Tuple      ,						)			->					Union[str, Any]:
										snake_case:    str      							={} if sp_model_kwargs is None else sp_model_kwargs

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

										try:
													import sentencepiece as spm
										except ImportError:
													logger.warning(
													    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
													    ' pip install sentencepiece'			)
													raise

										snake_case:    Optional[Any]      							=spm.SentencePieceProcessor(**self.sp_model_kwargs			)
										self.sp_model.Load(str(lowerCamelCase__			)			)
										snake_case:    Union[str, 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'

										# put special tokens and [unused] tokens into the vocab
										snake_case:    Tuple      							={"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

										for i in range(1_0			):
													snake_case:    Union[str, Any]      							=F'''[unused{i}]'''
													snake_case:    str      							=5 + i

										# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
										snake_case:    int      							=1_2
										snake_case:    List[str]      							={v: k for k, v in self.fairseq_tokens_to_ids.items()}
										for k in self.fairseq_tokens_to_ids.keys():
													self.unique_no_split_tokens.append(lowerCamelCase__			)


							def __getstate__(				self   :						Tuple			)			->					Tuple:
										snake_case:    List[Any]      							=self.__dict__.copy()
										snake_case:    List[str]      							=None
										return state


							def __setstate__(				self   :						List[str]      ,						a_   :						Dict			)			->					Tuple:
										snake_case:    Dict      							=d
										try:
													import sentencepiece as spm
										except ImportError:
													logger.warning(
													    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
													    ' pip install sentencepiece'			)
													raise

										# for backward compatibility
										if not hasattr(self      ,						'sp_model_kwargs'			):
													snake_case:    List[str]      							={}

										snake_case:    List[str]      							=spm.SentencePieceProcessor(**self.sp_model_kwargs			)
										self.sp_model.Load(self.vocab_file			)


							def    UpperCamelCase     (				self   :						str      ,						a_   :						List[int]      ,						a_   :						Optional[List[int]] = None      ,						a_   :						bool = False			)			->					Union[str, Any]:

										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 ([0] * len(lowerCamelCase__			)) + [1]
										return ([0] * len(lowerCamelCase__			)) + [1] + ([0] * len(lowerCamelCase__			)) + [1]


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						List[int]      ,						a_   :						Optional[List[int]] = None			)			->					Optional[Any]:
										snake_case:    Union[str, Any]      							=[self.sep_token_id]

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


							@property
							def    UpperCamelCase     (				self   :						List[str]			)			->					List[str]:
										return len(self.sp_model			) + self.fairseq_offset


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Optional[int]:
										snake_case:    List[str]      							={self.convert_ids_to_tokens(lowerCamelCase__			): i for i in range(self.vocab_size			)}
										vocab.update(self.added_tokens_encoder			)
										return vocab


							def    UpperCamelCase     (				self   :						int      ,						a_   :						str			)			->					Union[str, Any]:
										return self.sp_model.encode(lowerCamelCase__      ,						out_type=lowerCamelCase__			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						Optional[Any]			)			->					Optional[Any]:

										if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
										snake_case:    Dict      							=self.sp_model.PieceToId(lowerCamelCase__			)

										# 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    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						Optional[Any]			)			->					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    UpperCamelCase     (				self   :						Dict      ,						a_   :						List[str]			)			->					List[str]:
										snake_case:    Any      							="""""".join(lowerCamelCase__			).replace(lowerCamelCase__      ,						' '			).strip()
										return out_string


							def    UpperCamelCase     (				self   :						Dict      ,						a_   :						str      ,						a_   :						Optional[str] = None			)			->					Tuple:

										if not os.path.isdir(lowerCamelCase__			):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
													return
										snake_case:    Optional[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:
																snake_case:    Tuple      							=self.sp_model.serialized_model_proto()
																fi.write(lowerCamelCase__			)

										return (out_vocab_file,)




							def    UpperCamelCase     (				self   :						List[str]      ,						a_   :						List[int]      ,						a_   :						Optional[List[int]] = None			)			->					Union[str, Any]:

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












from __future__ import annotations


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					Tuple					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Tuple							)     ->  Optional[Any]:  # noqa: E741
   while r - l > 1:
      __UpperCamelCase	:			int					=					(l + r) // 2
      if v[m] >= key:
         __UpperCamelCase	:			Dict					=					m
      else:
         __UpperCamelCase	:			Optional[Any]					=					m  # noqa: E741
   return r


def 				__lowerCamelCase     (							__lowerCAmelCase							:					list[int]							)     ->  int:
   if len(__lowerCAmelCase							) == 0:
      return 0

   __UpperCamelCase	:			Optional[Any]					=					[0] * len(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					1

   __UpperCamelCase	:			Union[str, Any]					=					v[0]

   for i in range(1					,							len(__lowerCAmelCase							)							):
      if v[i] < tail[0]:
         __UpperCamelCase	:			Dict					=					v[i]
      elif v[i] > tail[length - 1]:
         __UpperCamelCase	:			Optional[int]					=					v[i]
         length += 1
      else:
         __UpperCamelCase	:			List[Any]					=					v[i]

   return length


if __name__ == "__main__":
       import doctest

       doctest.testmod()



