

"""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


snake_case_   :   Optional[Any]        =       logging.get_logger(__name__)

snake_case_   :   str        =       "▁"

snake_case_   :   int        =       {"vocab_file": "prophetnet.tokenizer"}

snake_case_   :   Union[str, Any]        =       {
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

snake_case_   :   str        =       {
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

snake_case_   :   Union[str, Any]        =       {
    "microsoft/xprophetnet-large-wiki100-cased": 5_1_2,
}



def   lowercase_	(    _lowercase       :		List[Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   Tuple      =				collections.OrderedDict()
					with open(_lowercase							,      "r"							,      encoding="utf-8"	) as reader:
										UpperCAmelCase			:   List[str]      =				reader.readlines()
					for index, token in enumerate(_lowercase	):
										UpperCAmelCase			:   Any      =				token.rstrip("\n"	)
										UpperCAmelCase			:   Optional[int]      =				index
					return vocab







class       snake_case__  ( _SCREAMING_SNAKE_CASE     ):
			SCREAMING_SNAKE_CASE__     				=						VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE__     				=						PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE__     				=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE__     				=						["input_ids", "attention_mask"]







			def __init__(			self			: List[str]			,  lowercase			: Optional[int]			,  lowercase			: Tuple="[SEP]"			,  lowercase			: Any="[SEP]"			,  lowercase			: Union[str, Any]="[SEP]"			,  lowercase			: Optional[Any]="[UNK]"			,  lowercase			: int="[PAD]"			,  lowercase			: str="[CLS]"			,  lowercase			: Optional[Any]="[MASK]"			,  lowercase			: Dict = None			,  **lowercase			: Any			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				{} if sp_model_kwargs is None else sp_model_kwargs

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

								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

								UpperCAmelCase			:   Optional[Any]      =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(str(A_  )  )
								UpperCAmelCase			:   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'

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

								for i in range(10  ):
													UpperCAmelCase			:   Union[str, Any]      =				f"""[unused{i}]"""
													UpperCAmelCase			:   List[Any]      =				5 + i

								# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
								UpperCAmelCase			:   Union[str, Any]      =				12
								UpperCAmelCase			:   Optional[int]      =				{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(A_  )







			def __getstate__(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.__dict__.copy()
								UpperCAmelCase			:   List[Any]      =				None
								return state







			def __setstate__(			self			: List[Any]			,  lowercase			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				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"  ):
													UpperCAmelCase			:   List[str]      =				{}

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







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: Any			,  lowercase			: Optional[Any] = None			,  lowercase			: Tuple = False  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: List[str]			,  lowercase			: Dict = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[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 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								return len(self.sp_model  ) + self.fairseq_offset







			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				{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			: Union[str, Any]			,  lowercase			: List[str]  ):





								'''simple docstring'''



								return self.sp_model.encode(A_			,  out_type=A_  )







			def 			__lowerCAmelCase				(			self			: Any			,  lowercase			: int  ):





								'''simple docstring'''



								if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
								UpperCAmelCase			:   Optional[int]      =				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			: Dict			,  lowercase			: int  ):





								'''simple docstring'''



								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			: List[str]			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				"".join(A_  ).replace(A_			,  " "  ).strip()
								return out_string







			def 			__lowerCAmelCase				(			self			: List[str]			,  lowercase			: List[str]			,  lowercase			: Dict = None  ):





								'''simple docstring'''



								if not os.path.isdir(A_  ):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
													return
								UpperCAmelCase			:   str      =				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			:   Union[str, Any]      =				self.sp_model.serialized_model_proto()
																		fi.write(A_  )

								return (out_vocab_file,)







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: Optional[int]			,  lowercase			: Any = None  ):





								'''simple docstring'''



								if token_ids_a is None:
													return token_ids_a + [self.sep_token_id]
								UpperCAmelCase			:   List[str]      =				[self.sep_token_id]
								return token_ids_a + sep + token_ids_a + sep





"""simple docstring"""






def   lowercase_	(    _lowercase       :		list							,      _lowercase       :		int							,      _lowercase       :		int = 0							,      _lowercase       :		int = 0	):




					'''simple docstring'''






					UpperCAmelCase			:   Tuple      =				right or len(_lowercase	) - 1
					if left > right:
										return -1
					elif list_data[left] == key:
										return left
					elif list_data[right] == key:
										return right
					else:
										return search(_lowercase							,      _lowercase							,      left + 1							,      right - 1	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


