import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam


class 	lowerCamelCase_     (   datasets.BeamBasedBuilder ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        return datasets.DatasetInfo(
            features=datasets.Features({'''content''': datasets.Value('''string''')}) , supervised_keys=__lowercase , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Optional[int]:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_dummy_examples()})]







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	List[str]:
        import apache_beam as beam

        return pipeline | "Load Examples" >> beam.Create(__lowercase)


class 	lowerCamelCase_     (   datasets.BeamBasedBuilder ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	Dict:
        return datasets.DatasetInfo(
            features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''')})}) , supervised_keys=__lowercase , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Dict:
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_nested_examples()})
        ]







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Optional[int]:
        import apache_beam as beam

        return pipeline | "Load Examples" >> beam.Create(__lowercase)



def lowerCamelCase  (   ):
    '''simple docstring'''







    return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar''']	)]



def lowerCamelCase  (   ):
    '''simple docstring'''







    return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar''']	)]


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    @require_beam
    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Any			  =		len(get_test_dummy_examples())
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
            __UpperCamelCase     :Optional[int]			  =		DummyBeamDataset(cache_dir=__lowercase , beam_runner='''DirectRunner''')
            builder.download_and_prepare()
            self.assertTrue(
                os.path.exists(
                    os.path.join(__lowercase , builder.name , '''default''' , '''0.0.0''' , f"""{builder.name}-train.arrow""")))
            self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''')}))
            __UpperCamelCase     :str			  =		builder.as_dataset()
            self.assertEqual(dset['''train'''].num_rows , __lowercase)
            self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , __lowercase)
            self.assertDictEqual(dset['''train'''][0] , get_test_dummy_examples()[0][1])
            self.assertDictEqual(
                dset['''train'''][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1])
            self.assertTrue(
                os.path.exists(os.path.join(__lowercase , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''')))
            del dset







    @require_beam
    def 			UpperCamelCase__   (	self)       ->	Any:
        import apache_beam as beam

        __UpperCamelCase     :int			  =		beam.io.parquetio.WriteToParquet

        __UpperCamelCase     :Optional[int]			  =		len(get_test_dummy_examples())
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
            __UpperCamelCase     :Optional[int]			  =		DummyBeamDataset(cache_dir=__lowercase , beam_runner='''DirectRunner''')
            with patch('''apache_beam.io.parquetio.WriteToParquet''') as write_parquet_mock:
                __UpperCamelCase     :List[Any]			  =		partial(__lowercase , num_shards=2)
                builder.download_and_prepare()
            self.assertTrue(
                os.path.exists(
                    os.path.join(
                        __lowercase , builder.name , '''default''' , '''0.0.0''' , f"""{builder.name}-train-00000-of-00002.arrow""")))
            self.assertTrue(
                os.path.exists(
                    os.path.join(
                        __lowercase , builder.name , '''default''' , '''0.0.0''' , f"""{builder.name}-train-00000-of-00002.arrow""")))
            self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''')}))
            __UpperCamelCase     :Dict			  =		builder.as_dataset()
            self.assertEqual(dset['''train'''].num_rows , __lowercase)
            self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , __lowercase)
            # Order is not preserved when sharding, so we just check that all the elements are there
            self.assertListEqual(sorted(dset['''train''']['''content''']) , sorted(['''foo''', '''bar''', '''foobar''']))
            self.assertTrue(
                os.path.exists(os.path.join(__lowercase , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''')))
            del dset







    @require_beam
    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
            __UpperCamelCase     :Optional[int]			  =		DummyBeamDataset(cache_dir=__lowercase)
            self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare)







    @require_beam
    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :Dict			  =		len(get_test_nested_examples())
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
            __UpperCamelCase     :Tuple			  =		NestedBeamDataset(cache_dir=__lowercase , beam_runner='''DirectRunner''')
            builder.download_and_prepare()
            self.assertTrue(
                os.path.exists(
                    os.path.join(__lowercase , builder.name , '''default''' , '''0.0.0''' , f"""{builder.name}-train.arrow""")))
            self.assertDictEqual(
                builder.info.features , datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''')})}))
            __UpperCamelCase     :Union[str, Any]			  =		builder.as_dataset()
            self.assertEqual(dset['''train'''].num_rows , __lowercase)
            self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , __lowercase)
            self.assertDictEqual(dset['''train'''][0] , get_test_nested_examples()[0][1])
            self.assertDictEqual(
                dset['''train'''][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1])
            self.assertTrue(
                os.path.exists(os.path.join(__lowercase , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''')))
            del dset








'''simple docstring'''



import numpy as np







def    a__       (  a__      ,    a__      ,    a__ = 1E-1_2      ,    a__ = 1_00      ,    ):




     """simple docstring"""


     assert np.shape(a__						)[0] == np.shape(a__						)[1]
     # Ensure proper dimensionality.
     assert np.shape(a__						)[0] == np.shape(a__						)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(a__						) == np.iscomplexobj(a__						)
     __SCREAMING_SNAKE_CASE     =      np.iscomplexobj(a__						)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(a__      ,    input_matrix.conj().T						)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     __SCREAMING_SNAKE_CASE     =      False
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      1E1_2

     while not convergence:
          # Multiple matrix by the vector.
          __SCREAMING_SNAKE_CASE     =      np.dot(a__      ,    a__						)
          # Normalize the resulting output vector.
          __SCREAMING_SNAKE_CASE     =      w / np.linalg.norm(a__						)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          __SCREAMING_SNAKE_CASE     =      vector.conj().T if is_complex else vector.T
          __SCREAMING_SNAKE_CASE     =      np.dot(a__      ,    np.dot(a__      ,    a__						)						)

          # Check convergence.
          __SCREAMING_SNAKE_CASE     =      np.abs(lambda_ - lambda_previous						) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               __SCREAMING_SNAKE_CASE     =      True

          __SCREAMING_SNAKE_CASE     =      lambda_

     if is_complex:
          __SCREAMING_SNAKE_CASE     =      np.real(lambda_						)

     return lambda_, vector







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]						)
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						)
     __SCREAMING_SNAKE_CASE     =      real_input_matrix.astype(np.complexaaa						)
     __SCREAMING_SNAKE_CASE     =      np.triu(1J * complex_input_matrix      ,    1						)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						).astype(np.complexaaa						)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               __SCREAMING_SNAKE_CASE     =      real_input_matrix
               __SCREAMING_SNAKE_CASE     =      real_vector
          elif problem_type == "complex":
               __SCREAMING_SNAKE_CASE     =      complex_input_matrix
               __SCREAMING_SNAKE_CASE     =      complex_vector

          # Our implementation.
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      power_iteration(a__      ,    a__						)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      np.linalg.eigh(a__						)
          # Last eigenvalue is the maximum one.
          __SCREAMING_SNAKE_CASE     =      eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          __SCREAMING_SNAKE_CASE     =      eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max						) <= 1E-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(a__						) - np.abs(a__						)						) <= 1E-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()






