
import logging

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

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


def 						UpperCAmelCase			(	a_						)		->	np.ndarray:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	np.ndarray:







				"""simple docstring"""



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

				return covariance_sum / features.shape[1]


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	np.ndarray:







				"""simple docstring"""



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

				return covariance_sum / features.shape[1]


def 						UpperCAmelCase			(	a_		,   a_						)		->	np.ndarray:







				"""simple docstring"""



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

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


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_						)		->	np.ndarray:







				"""simple docstring"""



				assert classes > dimensions

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

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


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]						)
				__A   			= np.array([0, 0, 0, 1, 1]						)
				__A   			= 2
				__A   			= 2

				# Assert that the function raises an AssertionError if dimensions > classes
				with pytest.raises(a_						) as error_info:
								__A   			= linear_discriminant_analysis(
								    a_		,   a_		,   a_		,   a_						)
								if isinstance(a_		,   np.ndarray						):
												raise AssertionError(
												    "Did not raise AssertionError for dimensions > classes"						)
								assert error_info.type is AssertionError


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]						)
				__A   			= 2
				__A   			= np.array([[6.92_820_323, 8.66_025_404, 10.39_230_485], [3.0, 3.0, 3.0]]						)

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()


import os
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 logging


SCREAMING_SNAKE_CASE							:Optional[int]							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Optional[int]							 =				{'vocab_file': 'sentencepiece.bpe.model'}

SCREAMING_SNAKE_CASE							:Tuple							 =				{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

SCREAMING_SNAKE_CASE							:List[Any]							 =				{
    'camembert-base': 512,
}

SCREAMING_SNAKE_CASE							:List[str]							 =				'▁'





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





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






	def __init__(       self					: Optional[Any]							,A					: List[str]							,A					: List[Any]="<s>"							,A					: Tuple="</s>"							,A					: Any="</s>"							,A					: Optional[Any]="<s>"							,A					: Tuple="<unk>"							,A					: str="<pad>"							,A					: int="<mask>"							,A					: Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]							,A					: Optional[Dict[str, Any]] = None							,**A					: Optional[Any]							,):
					# Mask token behave like a normal word, i.e. include the space before it
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else mask_token

					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=A							,eos_token=A							,unk_token=A							,sep_token=A							,cls_token=A							,pad_token=A							,mask_token=A							,additional_special_tokens=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)
					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(str(A ) )
					__A   			= vocab_file
					# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
					# sentencepiece vocabulary (this is the case for <s> and </s>
					__A   			= {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
					__A   			= len(self.fairseq_tokens_to_ids )
					__A   			= len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
					__A   			= {v: k for k, v in self.fairseq_tokens_to_ids.items()}






	def 				UpperCamelCase_  (       self					: int							,A					: List[int]							,A					: Optional[List[int]] = None ):

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






	def 				UpperCamelCase_  (       self					: Dict							,A					: List[int]							,A					: Optional[List[int]] = None							,A					: bool = False ):
					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 None:
									return [1] + ([0] * len(A )) + [1]
					return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: List[int]							,A					: Optional[List[int]] = None ):
					__A   			= [self.sep_token_id]
					__A   			= [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 + sep + token_ids_a + sep ) * [0]






	@property
	def 				UpperCamelCase_  (       self					: Dict ):
					return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def 				UpperCamelCase_  (       self					: Any							,A					: str ):
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Dict ):
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					elif self.sp_model.PieceToId(A ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
					return self.fairseq_offset + self.sp_model.PieceToId(A )






	def 				UpperCamelCase_  (       self					: Dict							,A					: Tuple ):
					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 				UpperCamelCase_  (       self					: Optional[Any]							,A					: Dict ):
					__A   			= []
					__A   			= ""
					__A   			= False
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													if not prev_is_special:
																	out_string += " "
													out_string += self.sp_model.decode(A ) + token
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )
					return out_string.strip()






	def __getstate__(       self					: Dict ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: Union[str, Any]							,A					: Any ):
					__A   			= d

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

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







	def 				UpperCamelCase_  (       self					: Any							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= 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:
													__A   			= self.sp_model.serialized_model_proto()
													fi.write(A )

					return (out_vocab_file,)
