


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							:					int     						=     logging.get_logger(__name__)

A							:					Optional[int]     						=     '▁'

A							:					int     						=     {'vocab_file': 'prophetnet.tokenizer'}

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

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

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





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






						__snake_case				=		collections.OrderedDict()
						with open(_lowerCAmelCase      ,	"r"      ,	encoding="utf-8" ) as reader:
												__snake_case				=		reader.readlines()
						for index, token in enumerate(_lowerCAmelCase ):
												__snake_case				=		token.rstrip("\n" )
												__snake_case				=		index
						return vocab






class     UpperCamelCase(  _a   ):
					snake_case_		:				Tuple										=	VOCAB_FILES_NAMES
					snake_case_		:				int										=	PRETRAINED_VOCAB_FILES_MAP
					snake_case_		:				int										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case_		:				Dict										=	["""input_ids""", """attention_mask"""]


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




											'''simple docstring'''

											__snake_case				=		{} if sp_model_kwargs is None else sp_model_kwargs

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

											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				=		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
											self.sp_model.Load(str(SCREAMING_SNAKE_CASE						)						)
											__snake_case				=		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				=		{"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

											for i in range(1_0						):
																	__snake_case				=		f'''[unused{i}]'''
																	__snake_case				=		5 + i

											# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
											__snake_case				=		1_2
											__snake_case				=		{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(SCREAMING_SNAKE_CASE						)


					def __getstate__(						self	:		Tuple						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.__dict__.copy()
											__snake_case				=		None
											return state


					def __setstate__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		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				=		{}

											__snake_case				=		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
											self.sp_model.Load(self.vocab_file						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		List[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[List[int]] = None     ,    SCREAMING_SNAKE_CASE	:		bool = False						)			->	List[int]:




											'''simple docstring'''

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


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




											'''simple docstring'''

											__snake_case				=		[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 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Tuple:




											'''simple docstring'''

											return len(self.sp_model						) + self.fairseq_offset


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		{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 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	str:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	List[str]:




											'''simple docstring'''

											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Any						)			->	str:




											'''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 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		"".join(SCREAMING_SNAKE_CASE						).replace(SCREAMING_SNAKE_CASE     ,    " "						).strip()
											return out_string


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Optional[str] = None						)			->	Tuple[str]:




											'''simple docstring'''

											if not os.path.isdir(SCREAMING_SNAKE_CASE						):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
																	return
											__snake_case				=		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				=		self.sp_model.serialized_model_proto()
																							fi.write(SCREAMING_SNAKE_CASE						)

											return (out_vocab_file,)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[List[int]] = None						)			->	List[int]:




											'''simple docstring'''

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









import itertools
import string
from collections.abc import Generator, Iterable





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Generator[tuple[str, ...], None, None]:


						'''simple docstring'''






						__snake_case				=		iter(_lowerCAmelCase )
						while True:
												__snake_case				=		tuple(itertools.islice(_lowerCAmelCase      ,	_lowerCAmelCase ) )
												if not chunk:
																		return
												yield chunk





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	str:


						'''simple docstring'''






						__snake_case				=		"".join([c.upper() for c in dirty if c in string.ascii_letters] )
						__snake_case				=		""

						if len(_lowerCAmelCase ) < 2:
												return dirty

						for i in range(len(_lowerCAmelCase ) - 1 ):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(_lowerCAmelCase ) & 1:
												clean += "X"

						return clean





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	list[str]:


						'''simple docstring'''






						__snake_case				=		"ABCDEFGHIKLMNOPQRSTUVWXYZ"
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						__snake_case				=		[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(_lowerCAmelCase )

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(_lowerCAmelCase )

						return table





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	str:


						'''simple docstring'''






						__snake_case				=		generate_table(_lowerCAmelCase )
						__snake_case				=		prepare_input(_lowerCAmelCase )
						__snake_case				=		""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowerCAmelCase      ,	2 ):
												__snake_case		,					__snake_case				=		divmod(table.index(_lowerCAmelCase )      ,	5 )
												__snake_case		,					__snake_case				=		divmod(table.index(_lowerCAmelCase )      ,	5 )

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	str:


						'''simple docstring'''






						__snake_case				=		generate_table(_lowerCAmelCase )
						__snake_case				=		""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowerCAmelCase      ,	2 ):
												__snake_case		,					__snake_case				=		divmod(table.index(_lowerCAmelCase )      ,	5 )
												__snake_case		,					__snake_case				=		divmod(table.index(_lowerCAmelCase )      ,	5 )

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext





