






"""simple docstring"""
import logging

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

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

def 							snake_case (  A__		):
			return input_array.reshape((input_array.size, 1)		)

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   np.nan
			for i in range(A__		):
						UpperCAmelCase_	:					List[Any]     	=   features[:, labels == i]
						UpperCAmelCase_	:					List[Any]     	=   data.mean(1		)
						# Centralize the data of class i
						UpperCAmelCase_	:					List[str]     	=   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)
									UpperCAmelCase_	:					Tuple     	=   np.dot(A__      ,centered_data.T		)

			return covariance_sum / features.shape[1]

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   features.mean(1		)
			UpperCAmelCase_	:					Any     	=   np.nan
			for i in range(A__		):
						UpperCAmelCase_	:					Optional[Any]     	=   features[:, labels == i]
						UpperCAmelCase_	:					List[Any]     	=   data.shape[1]
						UpperCAmelCase_	:					int     	=   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)
									UpperCAmelCase_	:					int     	=   device_data * np.dot(
									    column_reshape(A__		) - column_reshape(A__		)      ,(column_reshape(A__		) - column_reshape(A__		)).T      ,)

			return covariance_sum / features.shape[1]

def 							snake_case (  A__      ,A__		):

			# Check if the features have been loaded
			if features.any():
						UpperCAmelCase_	:					Optional[Any]     	=   features.mean(1		)
						# Center the dataset
						UpperCAmelCase_	:					Optional[Any]     	=   features - np.reshape(A__      ,(data_mean.size, 1)		)
						UpperCAmelCase_	:					Union[str, Any]     	=   np.dot(A__      ,centered_data.T		) / features.shape[1]
						UpperCAmelCase_	,      UpperCAmelCase_	:					List[Any]     	=   np.linalg.eigh(A__		)
						# Take all the columns in the reverse order (-1), and then takes only the first
						UpperCAmelCase_	:					List[str]     	=   eigenvectors[:, ::-1][:, 0:dimensions]
						# Project the database on the new space
						UpperCAmelCase_	:					List[Any]     	=   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 							snake_case (  A__      ,A__      ,A__      ,A__		):
			assert classes > dimensions

			# Check if features have been already loaded
			if features.any:
						UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   eigh(
						    covariance_between_classes(A__      ,A__      ,A__		)      ,covariance_within_classes(A__      ,A__      ,A__		)      ,)
						UpperCAmelCase_	:					Union[str, Any]     	=   eigenvectors[:, ::-1][:, :dimensions]
						UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   np.linalg.svd(A__		)
						UpperCAmelCase_	:					Optional[int]     	=   svd_matrix[:, 0:dimensions]
						UpperCAmelCase_	:					int     	=   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 							snake_case (  ):
			# Create dummy dataset with 2 classes and 3 features
			UpperCAmelCase_	:					Optional[Any]     	=   np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]		)
			UpperCAmelCase_	:					str     	=   np.array([0, 0, 0, 1, 1]		)
			UpperCAmelCase_	:					Dict     	=   2
			UpperCAmelCase_	:					List[str]     	=   2

			# Assert that the function raises an AssertionError if dimensions > classes
			with pytest.raises(A__		) as error_info:
						UpperCAmelCase_	:					Tuple     	=   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 							snake_case (  ):
			UpperCAmelCase_	:					Tuple     	=   np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
			UpperCAmelCase_	:					str     	=   2
			UpperCAmelCase_	:					Union[str, Any]     	=   np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]]		)

			with pytest.raises(A__		) as error_info:
						UpperCAmelCase_	:					Dict     	=   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()











"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			lowerCamelCase_							      = None


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_							      = {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase_							      = {
    '''camembert-base''': 512,
}

lowerCamelCase_							      = '''▁'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']
	__magic_name__       =							CamembertTokenizer





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

				super().__init__(
				    lowerCAmelCase_      ,      tokenizer_file=lowerCAmelCase_      ,      bos_token=lowerCAmelCase_      ,      eos_token=lowerCAmelCase_      ,      sep_token=lowerCAmelCase_      ,      cls_token=lowerCAmelCase_      ,      unk_token=lowerCAmelCase_      ,      pad_token=lowerCAmelCase_      ,      mask_token=lowerCAmelCase_      ,      additional_special_tokens=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				UpperCAmelCase_	:					str     	=   vocab_file
				UpperCAmelCase_	:					Optional[Any]     	=   False if not self.vocab_file else True





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:
				UpperCAmelCase_	:					List[Any]     	=   [self.sep_token_id]
				UpperCAmelCase_	:					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 + sep + token_ids_a + sep		) * [0]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     Tuple[str]:
				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."		)

				if not os.path.isdir(lowerCAmelCase_		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_	:					Dict     	=   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_		):
							copyfile(self.vocab_file      ,      lowerCAmelCase_		)

				return (out_vocab_file,)



