

"""simple docstring"""




import unittest

import numpy as np
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase = None				,     ):
 '''simple docstring'''





 UpperCAmelCase__       :	Any        =       np.shape(snake_case__   )
 UpperCAmelCase__       :	List[str]        =       np.shape(snake_case__   )
 UpperCAmelCase__       :	List[str]        =       np.shape(snake_case__   )

 if shape_a[0] != shape_b[0]:
  UpperCAmelCase__       :	int        =       (
      """Expected the same number of rows for A and B. """
      F"Instead found A of size {shape_a} and B of size {shape_b}"
  )
  raise ValueError(snake_case__   )

 if shape_b[1] != shape_c[1]:
  UpperCAmelCase__       :	List[str]        =       (
      """Expected the same number of columns for B and C. """
      F"Instead found B of size {shape_b} and C of size {shape_c}"
  )
  raise ValueError(snake_case__   )

 UpperCAmelCase__       :	Any        =       pseudo_inv
 if a_inv is None:
  try:
   UpperCAmelCase__       :	Optional[Any]        =       np.linalg.inv(snake_case__   )
  except np.linalg.LinAlgError:
   raise ValueError(
       """Input matrix A is not invertible. Cannot compute Schur complement."""   )

 return mat_c - mat_b.T @ a_inv @ mat_b







class   __lowercase      (     unittest.TestCase       ):

  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]     )
   UpperCAmelCase__       :	Union[str, Any]        =       np.array([[0, 3], [3, 0], [2, 3]]     )
   UpperCAmelCase__       :	List[Any]        =       np.array([[2, 1], [6, 3]]     )

   UpperCAmelCase__       :	int        =       schur_complement(a__       ,a__       ,a__     )

   UpperCAmelCase__       :	Union[str, Any]        =       np.block([[a, b], [b.T, c]]     )

   UpperCAmelCase__       :	Dict        =       np.linalg.det(a__     )
   UpperCAmelCase__       :	Tuple        =       np.linalg.det(a__     )
   UpperCAmelCase__       :	List[Any]        =       np.linalg.det(a__     )

   self.assertAlmostEqual(a__       ,det_a * det_s     )

  def  __lowercase							(	self		:      List[str]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[Any]        =       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]     )
   UpperCAmelCase__       :	Union[str, Any]        =       np.array([[0, 3], [3, 0], [2, 3]]     )
   UpperCAmelCase__       :	Optional[int]        =       np.array([[2, 1], [6, 3]]     )

   with self.assertRaises(a__     ):
    schur_complement(a__       ,a__       ,a__     )




  def  __lowercase							(	self		:      List[str]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]     )
   UpperCAmelCase__       :	List[str]        =       np.array([[0, 3], [3, 0], [2, 3]]     )
   UpperCAmelCase__       :	List[Any]        =       np.array([[2, 1, 3], [6, 3, 5]]     )

   with self.assertRaises(a__     ):
    schur_complement(a__       ,a__       ,a__     )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      unittest.main()













import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase__       		=  50_0000

lowerCamelCase__		,      lowerCamelCase__       		=  os.path.split(__file__)
lowerCamelCase__       		=  os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Any	):




     '''simple docstring'''






     __snake_case      :List[str] 							=    dataset.map(**snake_case__	)






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Dict	):




     '''simple docstring'''






     __snake_case      :int 							=    dataset.filter(**snake_case__	)






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :str 							=    {"""num examples""": SPEED_TEST_N_EXAMPLES}
     with tempfile.TemporaryDirectory() as tmp_dir:
          __snake_case      :Dict 							=    datasets.Features({"""text""": datasets.Value("""string"""	), """numbers""": datasets.Value("""float32"""	)}	)
          __snake_case      :Optional[int] 							=    generate_example_dataset(
              os.path.join(snake_case__						,"""dataset.arrow"""	)						,snake_case__						,num_examples=snake_case__	)

          __snake_case      :str 							=    transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""						,use_fast=snake_case__	)

          def tokenize(snake_case__  : Optional[int]	):
               return tokenizer(examples["""text"""]	)

          __snake_case      :Optional[Any] 							=    map(snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,batched=snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""numpy"""	):
               __snake_case      :int 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""pandas"""	):
               __snake_case      :List[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""torch"""						,columns="""numbers"""	):
               __snake_case      :str 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""tensorflow"""						,columns="""numbers"""	):
               __snake_case      :Optional[int] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          __snake_case      :Dict 							=    map(snake_case__						,function=snake_case__						,batched=snake_case__	)

          __snake_case      :Tuple 							=    filter(snake_case__	)

          # Activate later when tokenizer support batched inputs
          # with dataset.formatted_as(type='numpy'):
          #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

     with open(snake_case__						,"""wb"""	) as f:
          f.write(json.dumps(snake_case__	).encode("""utf-8"""	)	)


if __name__ == "__main__":  # useful to run the profiler
     benchmark_map_filter()


