

'''simple docstring'''

import logging

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

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




def       UpperCamelCase(     UpperCAmelCase_	):
 return input_array.reshape((input_array.size, 1)	)




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

 return covariance_sum / features.shape[1]




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_	):
 UpperCAmelCase       :	Union[str, Any]											= features.mean(1	)
 UpperCAmelCase       :	Union[str, Any]											= np.nan
 for i in range(SCREAMING_SNAKE_CASE__	):
  UpperCAmelCase       :	List[str]											= features[:, labels == i]
  UpperCAmelCase       :	Optional[Any]											= data.shape[1]
  UpperCAmelCase       :	Optional[Any]											= 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)
   UpperCAmelCase       :	Optional[int]											= 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       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_	):

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

 # Check if features have been already loaded
 if features.any:
  UpperCAmelCase					, UpperCAmelCase       :	List[Any]											= 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__	)  ,       )
  UpperCAmelCase       :	List[Any]											= eigenvectors[:, ::-1][:, :dimensions]
  UpperCAmelCase					, UpperCAmelCase					, UpperCAmelCase       :	Optional[int]											= np.linalg.svd(SCREAMING_SNAKE_CASE__	)
  UpperCAmelCase       :	Dict											= svd_matrix[:, 0:dimensions]
  UpperCAmelCase       :	List[str]											= 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       UpperCamelCase(     ):
 # Create dummy dataset with 2 classes and 3 features
 UpperCAmelCase       :	str											= np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]	)
 UpperCAmelCase       :	int											= np.array([0, 0, 0, 1, 1]	)
 UpperCAmelCase       :	List[Any]											= 2
 UpperCAmelCase       :	Dict											= 2

 # Assert that the function raises an AssertionError if dimensions > classes
 with pytest.raises(SCREAMING_SNAKE_CASE__	) as error_info:
  UpperCAmelCase       :	List[str]											= 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       UpperCamelCase(     ):
 UpperCAmelCase       :	List[str]											= np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]	)
 UpperCAmelCase       :	Optional[Any]											= 2
 UpperCAmelCase       :	Tuple											= 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:
  UpperCAmelCase       :	Tuple											= 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 packaging import version

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


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "yolos"






    def __init__(			self					,   A_=768					,   A_=12					,   A_=12					,   A_=3072					,   A_="gelu"					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=1E-12					,   A_=[512, 864]					,   A_=16					,   A_=3					,   A_=True					,   A_=100					,   A_=True					,   A_=False					,   A_=1					,   A_=5					,   A_=2					,   A_=5					,   A_=2					,   A_=0.1					,   **A_					,   )  ->       Any:
         super().__init__(**A_    )

         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=patch_size
         __UpperCamelCase    							=num_channels
         __UpperCamelCase    							=qkv_bias
         __UpperCamelCase    							=num_detection_tokens
         __UpperCamelCase    							=use_mid_position_embeddings
         __UpperCamelCase    							=auxiliary_loss
         # Hungarian matcher
         __UpperCamelCase    							=class_cost
         __UpperCamelCase    							=bbox_cost
         __UpperCamelCase    							=giou_cost
         # Loss coefficients
         __UpperCamelCase    							=bbox_loss_coefficient
         __UpperCamelCase    							=giou_loss_coefficient
         __UpperCamelCase    							=eos_coefficient






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str      =  version.parse("1.11"		)






    @property
    def _a					(			self    )  ->       Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]    )






    @property
    def _a					(			self    )  ->       float:
         return 1E-4






    @property
    def _a					(			self    )  ->       int:
         return 12





