'''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


__lowercase        =    logging.get_logger(__name__)

__lowercase        =    '''▁'''

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

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

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

__lowercase        =    {
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_1_2,
}






def 							snake_case__				(     _A:   int					)       ->      str:

		'''simple docstring'''


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






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			VOCAB_FILES_NAMES
			UpperCAmelCase_     :	Any						=			PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_     :	List[Any]						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_     :	int						=			['''input_ids''', '''attention_mask''']



			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase="[SEP]"			,       __lowerCAmelCase="[SEP]"			,       __lowerCAmelCase="[SEP]"			,       __lowerCAmelCase="[UNK]"			,       __lowerCAmelCase="[PAD]"			,       __lowerCAmelCase="[CLS]"			,       __lowerCAmelCase="[MASK]"			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    {} 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

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

					for i in range(10):
							lowerCAmelCase     =    f"[unused{i}]"
							lowerCAmelCase     =    5 + i

					# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
					lowerCAmelCase     =    12
					lowerCAmelCase     =    {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):




					"""simple docstring"""


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



			def __setstate__(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    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"""):
							lowerCAmelCase     =    {}

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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = False):




					"""simple docstring"""


					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 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase     =    [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 				a_			(  self):




					"""simple docstring"""


					return len(self.sp_model) + self.fairseq_offset



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    {self.convert_ids_to_tokens(__lowerCAmelCase): i for i in range(self.vocab_size)}
					vocab.update(self.added_tokens_encoder)
					return vocab



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


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



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
					lowerCAmelCase     =    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 				a_			(  self			,       __lowerCAmelCase):




					"""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 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    """""".join(__lowerCAmelCase).replace(__lowerCAmelCase			,       """ """).strip()
					return out_string



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None):




					"""simple docstring"""


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

					return (out_vocab_file,)







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None):




					"""simple docstring"""


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





'''simple docstring'''




def 							snake_case__				(     _A:   int					)       ->      list[int]:

		'''simple docstring'''


		if length <= 0 or not isinstance(_A ,  _A					):
				raise ValueError("""Length must be a positive integer."""					)
		return [n * (2 * n - 1) for n in range(_A					)]


if __name__ == "__main__":
						print(hexagonal_numbers(length=5))
						print(hexagonal_numbers(length=1_0))




