


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

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


a_					:    str          =    logging.getLogger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				__magic_name__=-1						)					->							Optional[Any]:
      # in NER datasets, the last column is usually reserved for NER label
      _a			      =							label_idx
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							List[InputExample]:
      if isinstance(__magic_name__      ,				__magic_name__						):
           _a			      =							mode.value
      _a			      =							os.path.join(__magic_name__      ,				f'{mode}.txt'						)
      _a			      =							1
      _a			      =							[]
      with open(__magic_name__      ,				encoding='utf-8'						) as f:
           _a			      =							[]
           _a			      =							[]
           for line in f:
                if line.startswith('-DOCSTART-'						) or line == "" or line == "\n":
                     if words:
                          examples.append(InputExample(guid=f'{mode}-{guid_index}'      ,				words=__magic_name__      ,				labels=__magic_name__						)						)
                          guid_index += 1
                          _a			      =							[]
                          _a			      =							[]
                else:
                     _a			      =							line.split(' '						)
                     words.append(splits[0]						)
                     if len(__magic_name__						) > 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=__magic_name__      ,				labels=__magic_name__						)						)
      return examples
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							0
      for line in test_input_reader:
           if line.startswith('-DOCSTART-'						) or line == "" or line == "\n":
                writer.write(__magic_name__						)
                if not preds_list[example_id]:
                     example_id += 1
           elif preds_list[example_id]:
                _a			      =							line.split()[0] + ' ' + preds_list[example_id].pop(0						) + '\n'
                writer.write(__magic_name__						)
           else:
                logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'      ,				line.split()[0]						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							List[str]:
      if path:
           with open(__magic_name__      ,				'r'						) as f:
                _a			      =							f.read().splitlines()
           if "O" not in labels:
                _a			      =							['O'] + labels
           return labels
      else:
           return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self						)					->							Any:
      # in CONLL2003 dataset chunk column is second-to-last
      super().__init__(label_idx=-2						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							List[str]:
      if path:
           with open(__magic_name__      ,				'r'						) as f:
                _a			      =							f.read().splitlines()
           if "O" not in labels:
                _a			      =							['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  a     (     _SCREAMING_SNAKE_CASE       ):
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							List[InputExample]:
      if isinstance(__magic_name__      ,				__magic_name__						):
           _a			      =							mode.value
      _a			      =							os.path.join(__magic_name__      ,				f'{mode}.txt'						)
      _a			      =							1
      _a			      =							[]

      with open(__magic_name__      ,				encoding='utf-8'						) as f:
           for sentence in parse_incr(__magic_name__						):
                _a			      =							[]
                _a			      =							[]
                for token in sentence:
                     words.append(token['form']						)
                     labels.append(token['upos']						)
                assert len(__magic_name__						) == len(__magic_name__						)
                if words:
                     examples.append(InputExample(guid=f'{mode}-{guid_index}'      ,				words=__magic_name__      ,				labels=__magic_name__						)						)
                     guid_index += 1
      return examples
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							0
      for sentence in parse_incr(__magic_name__						):
           _a			      =							preds_list[example_id]
           _a			      =							''
           for token in sentence:
                out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0						)}) '
           out += "\n"
           writer.write(__magic_name__						)
           example_id += 1




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							List[str]:
      if path:
           with open(__magic_name__      ,				'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 	_A				(lowerCAmelCase__  :np.ndarray		)   ->  np.ndarray:




     '''simple docstring'''



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






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



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

     return covariance_sum / features.shape[1]






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     _a			      =							features.mean(1		)
     _a			      =							np.nan
     for i in range(lowerCAmelCase__		):
          _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(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)				,						(column_reshape(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)).T				,						)
          else:
               # If covariance_sum is np.nan (i.e. first loop)
               _a			      =							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__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     if features.any():
          _a			      =							features.mean(1		)
          # Center the dataset
          _a			      =							features - np.reshape(lowerCAmelCase__				,						(data_mean.size, 1)		)
          _a			      =							np.dot(lowerCAmelCase__				,						centered_data.T		) / features.shape[1]
          _a      ,   _a			      =							np.linalg.eigh(lowerCAmelCase__		)
          # 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				,						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__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     assert classes > dimensions

     # Check if features have been already loaded
     if features.any:
          _a      ,   _a			      =							eigh(
              covariance_between_classes(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)				,						covariance_within_classes(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)				,						)
          _a			      =							eigenvectors[:, ::-1][:, :dimensions]
          _a      ,   _a      ,   _a			      =							np.linalg.svd(lowerCAmelCase__		)
          _a			      =							svd_matrix[:, 0:dimensions]
          _a			      =							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				()   ->  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(lowerCAmelCase__		) as error_info:
          _a			      =							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				()   ->  None:




     '''simple docstring'''



     _a			      =							np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
     _a			      =							2
     _a			      =							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(lowerCAmelCase__		) as error_info:
          _a			      =							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()

