




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

import sentencepiece as spm

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


a													=			"▁"

a													=			{"vocab_file": "spiece.model"}

a													=			{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

a													=			{
    "google/pegasus-xsum": 512,
}


a													=			logging.get_logger(__name__)




class       _A				(		__lowercase     ):
		__a					    =       VOCAB_FILES_NAMES

		__a					    =       VOCAB_FILES_NAMES
		__a					    =       PRETRAINED_VOCAB_FILES_MAP
		__a					    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a					    =       ["""input_ids""", """attention_mask"""]






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE="<pad>"						,		_SCREAMING_SNAKE_CASE="</s>"						,		_SCREAMING_SNAKE_CASE="<unk>"						,		_SCREAMING_SNAKE_CASE="<mask_2>"						,		_SCREAMING_SNAKE_CASE="<mask_1>"						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=103						,		_SCREAMING_SNAKE_CASE = None						,		**_SCREAMING_SNAKE_CASE						,		):
								_UpperCAmelCase      =			offset
								if additional_special_tokens is not None:
														if not isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
																				raise TypeError(
																				    F"additional_special_tokens should be of type {type(_SCREAMING_SNAKE_CASE			)}, but is"
																				    F" {type(_SCREAMING_SNAKE_CASE			)}"			)

														_UpperCAmelCase      =			(
														    ([mask_token_sent] + additional_special_tokens)
														    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
														    else additional_special_tokens
														)
														# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
														additional_special_tokens_extended += [
														    F"<unk_{i}>" for i in range(len(_SCREAMING_SNAKE_CASE			)						,		self.offset - 1			)
														]

														if len(set(_SCREAMING_SNAKE_CASE			)			) != len(_SCREAMING_SNAKE_CASE			):
																				raise ValueError(
																				    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
																				    F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."			)
														_UpperCAmelCase      =			additional_special_tokens_extended
								else:
														_UpperCAmelCase      =			[mask_token_sent] if mask_token_sent is not None else []
														additional_special_tokens += [F"<unk_{i}>" for i in range(2						,		self.offset			)]

								_UpperCAmelCase      =			{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=_SCREAMING_SNAKE_CASE						,		unk_token=_SCREAMING_SNAKE_CASE						,		mask_token=_SCREAMING_SNAKE_CASE						,		pad_token=_SCREAMING_SNAKE_CASE						,		mask_token_sent=_SCREAMING_SNAKE_CASE						,		offset=_SCREAMING_SNAKE_CASE						,		additional_special_tokens=_SCREAMING_SNAKE_CASE						,		sp_model_kwargs=self.sp_model_kwargs						,		**_SCREAMING_SNAKE_CASE						,		)
								_UpperCAmelCase      =			mask_token_sent
								_UpperCAmelCase      =			vocab_file
								_UpperCAmelCase      =			spm.SentencePieceProcessor(**self.sp_model_kwargs			)
								self.sp_model.Load(_SCREAMING_SNAKE_CASE			)

								# add special tokens to encoder dict
								_UpperCAmelCase      =			{
								    0: self.pad_token,
								    1: self.eos_token,
								}

								if self.mask_token_sent is not None:
														self.encoder.update(
														    {
														        2: self.mask_token_sent,
														        3: self.mask_token,
														    }			)

								if self.offset > 0:
														# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
														# mask_token_sent is already added to list -> so start at 1
														self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1						,		self.offset - 1			)}			)

								_UpperCAmelCase      =			{v: k for k, v in self.encoder.items()}






		@property
		def 				UpperCAmelCase (    self			):
								return len(self.sp_model			) + self.offset






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			{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 __getstate__(    self			):
								_UpperCAmelCase      =			self.__dict__.copy()
								_UpperCAmelCase      =			None
								return state






		def __setstate__(    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			d

								# for backward compatibility
								if not hasattr(self						,		"""sp_model_kwargs"""			):
														_UpperCAmelCase      =			{}

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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								return self.sp_model.encode(_SCREAMING_SNAKE_CASE						,		out_type=_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								if token in self.decoder:
														return self.decoder[token]
								elif token in self.added_tokens_decoder:
														return self.added_tokens_decoder[token]
								_UpperCAmelCase      =			self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE			)
								return sp_id + self.offset






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								if index in self.encoder:
														return self.encoder[index]
								elif index in self.added_tokens_encoder:
														return self.added_tokens_encoder[index]
								else:
														_UpperCAmelCase      =			self.sp_model.IdToPiece(index - self.offset			)
								return token






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			[]
								_UpperCAmelCase      =			""""""
								for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																				out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE			) + token
																				_UpperCAmelCase      =			[]
														else:
																				current_sub_tokens.append(_SCREAMING_SNAKE_CASE			)
								out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE			)
								return out_string.strip()






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=False			):
								return 1






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			set(self.all_special_ids			)  # call it once instead of inside list comp
								all_special_ids.remove(self.unk_token_id			)  # <unk> is only sometimes special

								return [1 if x in all_special_ids else 0 for x in seq]






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = False			):
								if already_has_special_tokens:
														return self._special_token_mask(_SCREAMING_SNAKE_CASE			)
								elif token_ids_a is None:
														return self._special_token_mask(_SCREAMING_SNAKE_CASE			) + [1]
								else:
														return self._special_token_mask(token_ids_a + token_ids_a			) + [1]






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
								if token_ids_a is None:
														return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
								if not os.path.isdir(_SCREAMING_SNAKE_CASE			):
														logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
														return
								_UpperCAmelCase      =			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:
																				_UpperCAmelCase      =			self.sp_model.serialized_model_proto()
																				fi.write(_SCREAMING_SNAKE_CASE			)

								return (out_vocab_file,)





import unittest

import numpy as np




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case = None			,     )						->				np.ndarray:
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )

						if shape_a[0] != shape_b[0]:
												_UpperCAmelCase      =			(
												    """Expected the same number of rows for A and B. """
												    f"Instead found A of size {shape_a} and B of size {shape_b}"
												)
												raise ValueError(snake_case       )

						if shape_b[1] != shape_c[1]:
												_UpperCAmelCase      =			(
												    """Expected the same number of columns for B and C. """
												    f"Instead found B of size {shape_b} and C of size {shape_c}"
												)
												raise ValueError(snake_case       )

						_UpperCAmelCase      =			pseudo_inv
						if a_inv is None:
												try:
																		_UpperCAmelCase      =			np.linalg.inv(snake_case       )
												except np.linalg.LinAlgError:
																		raise ValueError(
																		    """Input matrix A is not invertible. Cannot compute Schur complement."""       )

						return mat_c - mat_b.T @ a_inv @ mat_b




class       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								_UpperCAmelCase      =			schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			np.block([[a, b], [b.T, c]]			)

								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)

								self.assertAlmostEqual(_SCREAMING_SNAKE_CASE						,		det_a * det_s			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								with self.assertRaises(_SCREAMING_SNAKE_CASE			):
														schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1, 3], [6, 3, 5]]			)

								with self.assertRaises(_SCREAMING_SNAKE_CASE			):
														schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				unittest.main()