




'''simple docstring'''





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


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

_SCREAMING_SNAKE_CASE							:       List[str]						=			{"vocab_file": "spiece.model"}

_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    }
}

_SCREAMING_SNAKE_CASE							:       Dict						=			{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

# Segments (not really needed)
_SCREAMING_SNAKE_CASE							:       str						=			0
_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			1
_SCREAMING_SNAKE_CASE							:       Tuple						=			2
_SCREAMING_SNAKE_CASE							:       Dict						=			3
_SCREAMING_SNAKE_CASE							:       Optional[Any]						=			4




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Union[str, Any]							=   VOCAB_FILES_NAMES
			lowerCAmelCase_					:				Optional[Any]							=   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_					:				Any							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_					:				Any							=   "left"




			def __init__( self    ,							a__    ,							a__=False    ,							a__=True    ,							a__=False    ,							a__="<s>"    ,							a__="</s>"    ,							a__="<unk>"    ,							a__="<sep>"    ,							a__="<pad>"    ,							a__="<cls>"    ,							a__="<mask>"    ,							a__=["<eop>", "<eod>"]    ,							a__ = None    ,							**a__    ,							)    ->    None:

										'''simple docstring'''




										snake_case_      =				AddedToken(a__    ,							lstrip=a__    ,							rstrip=a__							) if isinstance(a__    ,							a__							) else mask_token

										snake_case_      =				{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    do_lower_case=a__    ,							remove_space=a__    ,							keep_accents=a__    ,							bos_token=a__    ,							eos_token=a__    ,							unk_token=a__    ,							sep_token=a__    ,							pad_token=a__    ,							cls_token=a__    ,							mask_token=a__    ,							additional_special_tokens=a__    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**a__    ,							)

										snake_case_      =				3

										snake_case_      =				do_lower_case
										snake_case_      =				remove_space
										snake_case_      =				keep_accents
										snake_case_      =				vocab_file

										snake_case_      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(a__							)




			@property
			def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

										'''simple docstring'''




										return len(self.sp_model							)




			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




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




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

										'''simple docstring'''




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




			def __setstate__( self    ,							a__							)    ->    Tuple:

										'''simple docstring'''




										snake_case_      =				d

										# 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        lowerCAmelCase__    ( self    ,							a__							)    ->    Optional[int]:

										'''simple docstring'''




										if self.remove_space:
																	snake_case_      =				" ".join(inputs.strip().split()							)
										else:
																	snake_case_      =				inputs
										snake_case_      =				outputs.replace("``"    ,							"\""							).replace("''"    ,							"\""							)

										if not self.keep_accents:
																	snake_case_      =				unicodedata.normalize("NFKD"    ,							a__							)
																	snake_case_      =				"".join([c for c in outputs if not unicodedata.combining(a__							)]							)
										if self.do_lower_case:
																	snake_case_      =				outputs.lower()

										return outputs




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    List[str]:

										'''simple docstring'''




										snake_case_      =				self.preprocess_text(a__							)
										snake_case_      =				self.sp_model.encode(a__    ,							out_type=a__							)
										snake_case_      =				[]
										for piece in pieces:
																	if len(a__							) > 1 and piece[-1] == str(","							) and piece[-2].isdigit():
																								snake_case_      =				self.sp_model.EncodeAsPieces(piece[:-1].replace(a__    ,							""							)							)
																								if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																															if len(cur_pieces[0]							) == 1:
																																						snake_case_      =				cur_pieces[1:]
																															else:
																																						snake_case_      =				cur_pieces[0][1:]
																								cur_pieces.append(piece[-1]							)
																								new_pieces.extend(a__							)
																	else:
																								new_pieces.append(a__							)

										return new_pieces




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Union[str, Any]:

										'''simple docstring'''




										return self.sp_model.PieceToId(a__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Optional[Any]:

										'''simple docstring'''




										return self.sp_model.IdToPiece(a__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    List[Any]:

										'''simple docstring'''




										snake_case_      =				"".join(a__							).replace(a__    ,							" "							).strip()
										return out_string




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = False    ,							a__ = None    ,							a__ = True    ,							**a__    ,							)    ->    str:

										'''simple docstring'''




										snake_case_      =				kwargs.pop("use_source_tokenizer"    ,							a__							)

										snake_case_      =				self.convert_ids_to_tokens(a__    ,							skip_special_tokens=a__							)

										# To avoid mixing byte-level and unicode for byte-level BPT
										# we need to build string separately for added tokens and byte-level tokens
										# cf. https://github.com/huggingface/transformers/issues/1133
										snake_case_      =				[]
										snake_case_      =				[]
										for token in filtered_tokens:
																	if skip_special_tokens and token in self.all_special_ids:
																								continue
																	if token in self.added_tokens_encoder:
																								if current_sub_text:
																															sub_texts.append(self.convert_tokens_to_string(a__							)							)
																															snake_case_      =				[]
																								sub_texts.append(a__							)
																	else:
																								current_sub_text.append(a__							)
										if current_sub_text:
																	sub_texts.append(self.convert_tokens_to_string(a__							)							)

										# Mimic the behavior of the Rust tokenizer:
										# By default, there are no spaces between special tokens
										snake_case_      =				"".join(a__							)

										snake_case_      =				(
										    clean_up_tokenization_spaces
										    if clean_up_tokenization_spaces is not None
										    else self.clean_up_tokenization_spaces
										)
										if clean_up_tokenization_spaces:
																	snake_case_      =				self.clean_up_tokenization(a__							)
																	return clean_text
										else:
																	return text




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

										'''simple docstring'''




										snake_case_      =				[self.sep_token_id]
										snake_case_      =				[self.cls_token_id]
										if token_ids_a is None:
																	return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None    ,							a__ = False							)    ->    List[int]:

										'''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 not None:
																	return ([0] * len(a__							)) + [1] + ([0] * len(a__							)) + [1, 1]
										return ([0] * len(a__							)) + [1, 1]




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

										'''simple docstring'''




										snake_case_      =				[self.sep_token_id]
										snake_case_      =				[2]

										if token_ids_a is None:
																	return len(token_ids_a + sep							) * [0] + cls_segment_id
										return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    Tuple[str]:

										'''simple docstring'''




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

										return (out_vocab_file,)










'''simple docstring'''





from __future__ import annotations

import numpy as np


def 					UpperCamelCase_(     snake_case							:	np.ndarray					):

							'''simple docstring'''






							snake_case_ ,				snake_case_      =				np.shape(snake_case					)
							if rows != columns:
														snake_case_      =				(
														    "'table' has to be of square shaped array but got a "
														    f'{rows}x{columns} array:\n{table}'
														)
														raise ValueError(snake_case					)

							snake_case_      =				np.zeros((rows, columns)					)
							snake_case_      =				np.zeros((rows, columns)					)
							for i in range(snake_case					):
														for j in range(snake_case					):
																					snake_case_      =				sum(lower[i][k] * upper[k][j] for k in range(snake_case					)					)
																					if upper[j][j] == 0:
																												raise ArithmeticError("No LU decomposition exists"					)
																					snake_case_      =				(table[i][j] - total) / upper[j][j]
														snake_case_      =				1
														for j in range(snake_case							,    snake_case					):
																					snake_case_      =				sum(lower[i][k] * upper[k][j] for k in range(snake_case					)					)
																					snake_case_      =				table[i][j] - total
							return lower, upper


if __name__ == "__main__":
		import doctest

		doctest.testmod()




