


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(SCREAMING_SNAKE_CASE__):
								_SCREAMING_SNAKE_CASE			  =features[:, labels == i]
								_SCREAMING_SNAKE_CASE			  =data.mean(1)
								# Centralize the data of class i
								_SCREAMING_SNAKE_CASE			  =data - column_reshape(SCREAMING_SNAKE_CASE__)
								if i > 0:
												# If covariance_sum is not None
												covariance_sum += np.dot(SCREAMING_SNAKE_CASE__	,centered_data.T)
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												_SCREAMING_SNAKE_CASE			  =np.dot(SCREAMING_SNAKE_CASE__	,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(SCREAMING_SNAKE_CASE__):
								_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(SCREAMING_SNAKE_CASE__) - column_reshape(SCREAMING_SNAKE_CASE__)	,(column_reshape(SCREAMING_SNAKE_CASE__) - column_reshape(SCREAMING_SNAKE_CASE__)).T	,)
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												_SCREAMING_SNAKE_CASE			  =device_data * np.dot(
												    column_reshape(SCREAMING_SNAKE_CASE__) - column_reshape(SCREAMING_SNAKE_CASE__)	,(column_reshape(SCREAMING_SNAKE_CASE__) - column_reshape(SCREAMING_SNAKE_CASE__)).T	,)

				return covariance_sum / features.shape[1]







def 						lowerCamelCase(   a__	,a__):





				if features.any():
								_SCREAMING_SNAKE_CASE			  =features.mean(1)
								# Center the dataset
								_SCREAMING_SNAKE_CASE			  =features - np.reshape(SCREAMING_SNAKE_CASE__	,(data_mean.size, 1))
								_SCREAMING_SNAKE_CASE			  =np.dot(SCREAMING_SNAKE_CASE__	,centered_data.T) / features.shape[1]
								_SCREAMING_SNAKE_CASE			  =np.linalg.eigh(SCREAMING_SNAKE_CASE__)
								# 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	,SCREAMING_SNAKE_CASE__)
								logging.info('''Principal Component Analysis computed''')

								return projected_data
				else:
								logging.basicConfig(level=logging.ERROR	,format='''%(message)s'''	,force=SCREAMING_SNAKE_CASE__)
								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			  =eigh(
								    covariance_between_classes(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__)	,covariance_within_classes(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__)	,)
								_SCREAMING_SNAKE_CASE			  =eigenvectors[:, ::-1][:, :dimensions]
								_SCREAMING_SNAKE_CASE			  =np.linalg.svd(SCREAMING_SNAKE_CASE__)
								_SCREAMING_SNAKE_CASE			  =svd_matrix[:, 0:dimensions]
								_SCREAMING_SNAKE_CASE			  =np.dot(filtered_svd_matrix.T	,SCREAMING_SNAKE_CASE__)
								logging.info('''Linear Discriminant Analysis computed''')

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







def 						lowerCamelCase(   ):
				_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(SCREAMING_SNAKE_CASE__) as error_info:
								_SCREAMING_SNAKE_CASE			  =linear_discriminant_analysis(
								    SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__)
								if isinstance(SCREAMING_SNAKE_CASE__	,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([[6.9282_0323, 8.6602_5404, 10.3923_0485], [3.0, 3.0, 3.0]])

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


if __name__ == "__main__":
							import doctest

							doctest.testmod()



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_						:			str													=							logging.get_logger(__name__)

snake_case_						:			str													=							{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"data2vec-text"




						def __init__(       self					:      Optional[int]				,    _a					:      Union[str, Any]=3_0522				,    _a					:      Any=768				,    _a					:      List[Any]=12				,    _a					:      Any=12				,    _a					:      List[str]=3072				,    _a					:      Optional[Any]="gelu"				,    _a					:      List[str]=0.1				,    _a					:      Optional[Any]=0.1				,    _a					:      Optional[Any]=512				,    _a					:      Any=2				,    _a					:      Optional[Any]=0.02				,    _a					:      Dict=1E-12				,    _a					:      int=1				,    _a					:      Any=0				,    _a					:      List[Any]=2				,    _a					:      Dict="absolute"				,    _a					:      Optional[Any]=True				,    _a					:      Dict=None				,    **_a					:      Optional[Any]				,    )							->     Union[str, Any]:


										"""simple docstring"""





										super().__init__(pad_token_id=_a				,    bos_token_id=_a				,    eos_token_id=_a				,    **_a  )

										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =intermediate_size
										_SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings
										_SCREAMING_SNAKE_CASE			  =type_vocab_size
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =layer_norm_eps
										_SCREAMING_SNAKE_CASE			  =position_embedding_type
										_SCREAMING_SNAKE_CASE			  =use_cache
										_SCREAMING_SNAKE_CASE			  =classifier_dropout

class 						A__							(		UpperCamelCase__  ):




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.task == "multiple-choice":
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]  )