



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

import sentencepiece as spm

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


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{"vocab_file": "sentencepiece.model"}

_snake_case	=						{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

_snake_case	=						{
    "google/rembert": 256,
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	VOCAB_FILES_NAMES
							lowerCamelCase__         =	PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase__         =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







							def __init__(     self,    __a,    __a=False,    __a=True,    __a=True,    __a="[CLS]",    __a="[SEP]",    __a="[UNK]",    __a="[SEP]",    __a="[PAD]",    __a="[CLS]",    __a="[MASK]",    **__a,    ):

														'''simple docstring'''

														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,    **__a,    )

														_lowerCAmelCase  :							str				     =					do_lower_case
														_lowerCAmelCase  :							Union[str, Any]				     =					remove_space
														_lowerCAmelCase  :							Any				     =					keep_accents
														_lowerCAmelCase  :							Any				     =					vocab_file

														_lowerCAmelCase  :							List[Any]				     =					spm.SentencePieceProcessor()
														self.sp_model.Load(__a)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return len(self.sp_model)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					{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):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					self.__dict__.copy()
														_lowerCAmelCase  :							str				     =					None
														return state







							def __setstate__(     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					d
														_lowerCAmelCase  :							str				     =					spm.SentencePieceProcessor()
														self.sp_model.Load(self.vocab_file)







							def      snake_case__   (     self,    __a,    __a=False):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					self.sp_model.EncodeAsPieces(__a)
														return pieces







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														return self.sp_model.PieceToId(__a)







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														return self.sp_model.IdToPiece(__a)







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					self.sp_model.decode_pieces(__a)
														return out_string







							def      snake_case__   (     self,    __a,    __a = None):

														'''simple docstring'''

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







							def      snake_case__   (     self,    __a,    __a = None,    __a = False):

														'''simple docstring'''

														if already_has_special_tokens:
																					if token_ids_a is not None:
																												raise ValueError(
																												    "You should not supply a second sequence if the provided sequence of "
																												    "ids is already formatted with special tokens for the model.")
																					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

														if token_ids_a is not None:
																					return [1] + ([0] * len(__a)) + [1] + ([0] * len(__a)) + [1]
														return [1] + ([0] * len(__a)) + [1]







							def      snake_case__   (     self,    __a,    __a = None):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					[self.sep_token_id]
														_lowerCAmelCase  :							Optional[int]				     =					[self.cls_token_id]

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







							def      snake_case__   (     self,    __a,    __a = None):

														'''simple docstring'''

														if not os.path.isdir(__a):
																					logger.error("Vocabulary path ({}) should be a directory".format(__a))
																					return
														_lowerCAmelCase  :							Union[str, Any]				     =					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):
																					copyfile(self.vocab_file,    __a)

														return (out_vocab_file,)





import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="%(message)s")






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							return input_array.reshape((input_array.size, 1)						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					np.nan
							for i in range(_lowerCamelCase						):
														_lowerCAmelCase  :							Tuple				     =					features[:, labels == i]
														_lowerCAmelCase  :							Dict				     =					data.mean(1						)
														# Centralize the data of class i
														_lowerCAmelCase  :							Union[str, Any]				     =					data - column_reshape(_lowerCamelCase						)
														if i > 0:
																					# If covariance_sum is not None
																					covariance_sum += np.dot(_lowerCamelCase       ,				centered_data.T						)
														else:
																					# If covariance_sum is np.nan (i.e. first loop)
																					_lowerCAmelCase  :							int				     =					np.dot(_lowerCamelCase       ,				centered_data.T						)

							return covariance_sum / features.shape[1]






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					features.mean(1						)
							_lowerCAmelCase  :							List[str]				     =					np.nan
							for i in range(_lowerCamelCase						):
														_lowerCAmelCase  :							str				     =					features[:, labels == i]
														_lowerCAmelCase  :							Optional[Any]				     =					data.shape[1]
														_lowerCAmelCase  :							Optional[Any]				     =					data.mean(1						)
														if i > 0:
																					# If covariance_sum is not None
																					covariance_sum += device_data * np.dot(
																					    column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)       ,				(column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)).T       ,				)
														else:
																					# If covariance_sum is np.nan (i.e. first loop)
																					_lowerCAmelCase  :							Optional[Any]				     =					device_data * np.dot(
																					    column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)       ,				(column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)).T       ,				)

							return covariance_sum / features.shape[1]






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if features.any():
														_lowerCAmelCase  :							List[Any]				     =					features.mean(1						)
														# Center the dataset
														_lowerCAmelCase  :							List[Any]				     =					features - np.reshape(_lowerCamelCase       ,				(data_mean.size, 1)						)
														_lowerCAmelCase  :							Optional[Any]				     =					np.dot(_lowerCamelCase       ,				centered_data.T						) / features.shape[1]
														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[Any]				     =					np.linalg.eigh(_lowerCamelCase						)
														# Take all the columns in the reverse order (-1), and then takes only the first
														_lowerCAmelCase  :							Union[str, Any]				     =					eigenvectors[:, ::-1][:, 0:dimensions]
														# Project the database on the new space
														_lowerCAmelCase  :							List[Any]				     =					np.dot(filtered_eigenvectors.T       ,				_lowerCamelCase						)
														logging.info("Principal Component Analysis computed"						)

														return projected_data
							else:
														logging.basicConfig(level=logging.ERROR       ,				format="%(message)s"       ,				force=_lowerCamelCase						)
														logging.error("Dataset empty"						)
														raise AssertionError






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							assert classes > dimensions

							# Check if features have been already loaded
							if features.any:
														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					eigh(
														    covariance_between_classes(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)       ,				covariance_within_classes(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)       ,				)
														_lowerCAmelCase  :							List[str]				     =					eigenvectors[:, ::-1][:, :dimensions]
														_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase  :							Any				     =					np.linalg.svd(_lowerCamelCase						)
														_lowerCAmelCase  :							Optional[Any]				     =					svd_matrix[:, 0:dimensions]
														_lowerCAmelCase  :							str				     =					np.dot(filtered_svd_matrix.T       ,				_lowerCamelCase						)
														logging.info("Linear Discriminant Analysis computed"						)

														return projected_data
							else:
														logging.basicConfig(level=logging.ERROR       ,				format="%(message)s"       ,				force=_lowerCamelCase						)
														logging.error("Dataset empty"						)
														raise AssertionError






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[int]				     =					np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]						)
							_lowerCAmelCase  :							List[Any]				     =					np.array([0, 0, 0, 1, 1]						)
							_lowerCAmelCase  :							List[Any]				     =					2
							_lowerCAmelCase  :							Union[str, Any]				     =					2

							# Assert that the function raises an AssertionError if dimensions > classes
							with pytest.raises(_lowerCamelCase						) as error_info:
														_lowerCAmelCase  :							Union[str, Any]				     =					linear_discriminant_analysis(
														    _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)
														if isinstance(_lowerCamelCase       ,				np.ndarray						):
																					raise AssertionError(
																					    "Did not raise AssertionError for dimensions > classes"						)
														assert error_info.type is AssertionError






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]						)
							_lowerCAmelCase  :							List[str]				     =					2
							_lowerCAmelCase  :							List[Any]				     =					np.array([[6.92_82_03_23, 8.66_02_54_04, 10.39_23_04_85], [3.0, 3.0, 3.0]]						)

							with pytest.raises(_lowerCamelCase						) as error_info:
														_lowerCAmelCase  :							Tuple				     =					principal_component_analysis(_lowerCamelCase       ,				_lowerCamelCase						)
														if not np.allclose(_lowerCamelCase       ,				_lowerCamelCase						):
																					raise AssertionError
														assert error_info.type is AssertionError


if __name__ == "__main__":
						import doctest

						doctest.testmod()
