
"""simple docstring"""




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowerCAmelCase__				=					logging.getLogger(__name__)





class 			snake_case  (				__lowercase ):

							def __init__(self							,							SCREAMING_SNAKE_CASE_=-1     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			label_idx

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			mode.value
									SCREAMING_SNAKE_CASE_    			=			os.path.join(SCREAMING_SNAKE_CASE_							,							f'{mode}.txt'     )
									SCREAMING_SNAKE_CASE_    			=			1
									SCREAMING_SNAKE_CASE_    			=			[]
									with open(SCREAMING_SNAKE_CASE_							,							encoding='''utf-8'''     ) as f:
											SCREAMING_SNAKE_CASE_    			=			[]
											SCREAMING_SNAKE_CASE_    			=			[]
											for line in f:
													if line.startswith('''-DOCSTART-'''     ) or line == "" or line == "\n":
															if words:
																	examples.append(InputExample(guid=f'{mode}-{guid_index}'							,							words=SCREAMING_SNAKE_CASE_							,							labels=SCREAMING_SNAKE_CASE_     )     )
																	guid_index += 1
																	SCREAMING_SNAKE_CASE_    			=			[]
																	SCREAMING_SNAKE_CASE_    			=			[]
													else:
															SCREAMING_SNAKE_CASE_    			=			line.split(''' '''     )
															words.append(splits[0]     )
															if len(SCREAMING_SNAKE_CASE_     ) > 1:
																	labels.append(splits[self.label_idx].replace('''\n'''							,							''''''     )     )
															else:
																	# Examples could have no label for mode = "test"
																	labels.append('''O'''     )
											if words:
													examples.append(InputExample(guid=f'{mode}-{guid_index}'							,							words=SCREAMING_SNAKE_CASE_							,							labels=SCREAMING_SNAKE_CASE_     )     )
									return examples

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			0
									for line in test_input_reader:
											if line.startswith('''-DOCSTART-'''     ) or line == "" or line == "\n":
													writer.write(SCREAMING_SNAKE_CASE_     )
													if not preds_list[example_id]:
															example_id += 1
											elif preds_list[example_id]:
													SCREAMING_SNAKE_CASE_    			=			line.split()[0] + ''' ''' + preds_list[example_id].pop(0     ) + '''\n'''
													writer.write(SCREAMING_SNAKE_CASE_     )
											else:
													logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''							,							line.split()[0]     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if path:
											with open(SCREAMING_SNAKE_CASE_							,							'''r'''     ) as f:
													SCREAMING_SNAKE_CASE_    			=			f.read().splitlines()
											if "O" not in labels:
													SCREAMING_SNAKE_CASE_    			=			['''O'''] + labels
											return labels
									else:
											return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class 			snake_case  (				__lowercase ):

							def __init__(self     ):

									"""simple docstring"""







									super().__init__(label_idx=-2     )







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if path:
											with open(SCREAMING_SNAKE_CASE_							,							'''r'''     ) as f:
													SCREAMING_SNAKE_CASE_    			=			f.read().splitlines()
											if "O" not in labels:
													SCREAMING_SNAKE_CASE_    			=			['''O'''] + labels
											return labels
									else:
											return [
											    "O",
											    "B-ADVP",
											    "B-INTJ",
											    "B-LST",
											    "B-PRT",
											    "B-NP",
											    "B-SBAR",
											    "B-VP",
											    "B-ADJP",
											    "B-CONJP",
											    "B-PP",
											    "I-ADVP",
											    "I-INTJ",
											    "I-LST",
											    "I-PRT",
											    "I-NP",
											    "I-SBAR",
											    "I-VP",
											    "I-ADJP",
											    "I-CONJP",
											    "I-PP",
											]







class 			snake_case  (				__lowercase ):

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if isinstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			mode.value
									SCREAMING_SNAKE_CASE_    			=			os.path.join(SCREAMING_SNAKE_CASE_							,							f'{mode}.txt'     )
									SCREAMING_SNAKE_CASE_    			=			1
									SCREAMING_SNAKE_CASE_    			=			[]

									with open(SCREAMING_SNAKE_CASE_							,							encoding='''utf-8'''     ) as f:
											for sentence in parse_incr(SCREAMING_SNAKE_CASE_     ):
													SCREAMING_SNAKE_CASE_    			=			[]
													SCREAMING_SNAKE_CASE_    			=			[]
													for token in sentence:
															words.append(token['''form''']     )
															labels.append(token['''upos''']     )
													assert len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     )
													if words:
															examples.append(InputExample(guid=f'{mode}-{guid_index}'							,							words=SCREAMING_SNAKE_CASE_							,							labels=SCREAMING_SNAKE_CASE_     )     )
															guid_index += 1
									return examples

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			0
									for sentence in parse_incr(SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			preds_list[example_id]
											SCREAMING_SNAKE_CASE_    			=			''''''
											for token in sentence:
													out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0     )}) '
											out += "\n"
											writer.write(SCREAMING_SNAKE_CASE_     )
											example_id += 1







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if path:
											with open(SCREAMING_SNAKE_CASE_							,							'''r'''     ) as f:
													return f.read().splitlines()
									else:
											return [
											    "ADJ",
											    "ADP",
											    "ADV",
											    "AUX",
											    "CCONJ",
											    "DET",
											    "INTJ",
											    "NOUN",
											    "NUM",
											    "PART",
											    "PRON",
											    "PROPN",
											    "PUNCT",
											    "SCONJ",
											    "SYM",
											    "VERB",
											    "X",
											]

"""simple docstring"""




import logging

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

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



def 							_lowerCamelCase			(			__a     ):
		return input_array.reshape((input_array.size, 1)     )



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			np.nan
		for i in range(__a     ):
				SCREAMING_SNAKE_CASE_    			=			features[:, labels == i]
				SCREAMING_SNAKE_CASE_    			=			data.mean(1     )
				# Centralize the data of class i
				SCREAMING_SNAKE_CASE_    			=			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)
						SCREAMING_SNAKE_CASE_    			=			np.dot(__a,				centered_data.T     )

		return covariance_sum / features.shape[1]



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			features.mean(1     )
		SCREAMING_SNAKE_CASE_    			=			np.nan
		for i in range(__a     ):
				SCREAMING_SNAKE_CASE_    			=			features[:, labels == i]
				SCREAMING_SNAKE_CASE_    			=			data.shape[1]
				SCREAMING_SNAKE_CASE_    			=			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)
						SCREAMING_SNAKE_CASE_    			=			device_data * np.dot(
						    column_reshape(__a     ) - column_reshape(__a     ),				(column_reshape(__a     ) - column_reshape(__a     )).T,				)

		return covariance_sum / features.shape[1]



def 							_lowerCamelCase			(			__a,				__a     ):

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

		# Check if features have been already loaded
		if features.any:
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			eigh(
				    covariance_between_classes(__a,				__a,				__a     ),				covariance_within_classes(__a,				__a,				__a     ),				)
				SCREAMING_SNAKE_CASE_    			=			eigenvectors[:, ::-1][:, :dimensions]
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			np.linalg.svd(__a     )
				SCREAMING_SNAKE_CASE_    			=			svd_matrix[:, 0:dimensions]
				SCREAMING_SNAKE_CASE_    			=			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 							_lowerCamelCase			(			):
		# Create dummy dataset with 2 classes and 3 features
		SCREAMING_SNAKE_CASE_    			=			np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]     )
		SCREAMING_SNAKE_CASE_    			=			np.array([0, 0, 0, 1, 1]     )
		SCREAMING_SNAKE_CASE_    			=			2
		SCREAMING_SNAKE_CASE_    			=			2

		# Assert that the function raises an AssertionError if dimensions > classes
		with pytest.raises(__a     ) as error_info:
				SCREAMING_SNAKE_CASE_    			=			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 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]     )
		SCREAMING_SNAKE_CASE_    			=			2
		SCREAMING_SNAKE_CASE_    			=			np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]     )

		with pytest.raises(__a     ) as error_info:
				SCREAMING_SNAKE_CASE_    			=			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()