



'''simple docstring'''


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase							       =			50_000
lowercase							       =			5_000

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






@get_duration
def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     for i in range(lowercase__       ):
          a_       		=dataset[i]






@get_duration
def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     for i in range(0       ,	len(lowercase__       )       ,	lowercase__       ):
          a_       		=dataset[i : i + batch_size]






@get_duration
def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     with dataset.formatted_as(type=lowercase__       ):
          for i in range(lowercase__       ):
               a_       		=dataset[i]






@get_duration
def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     with dataset.formatted_as(type=lowercase__       ):
          for i in range(0       ,	lowercase__       ,	lowercase__       ):
               a_       		=dataset[i : i + batch_size]






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		={"num examples": SPEED_TEST_N_EXAMPLES}
     a_       		=[
         (read, {"length": SMALL_TEST}),
         (read, {"length": SPEED_TEST_N_EXAMPLES}),
         (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_0}),
         (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_0_0}),
         (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_0_0_0}),
         (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
         (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
         (read_formatted, {"type": "torch", "length": SMALL_TEST}),
         (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
         (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_0}),
         (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_0_0_0}),
     ]

     a_       		=[
         (read, {"length": SMALL_TEST}),
         (read, {"length": SPEED_TEST_N_EXAMPLES}),
         (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_0}),
         (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_0_0}),
         (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_0_0_0}),
         (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
         (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_0}),
         (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_0_0_0}),
     ]
     with tempfile.TemporaryDirectory() as tmp_dir:
          print("generating dataset"       )
          a_       		=datasets.Features(
              {"list": datasets.Sequence(datasets.Value("float32"       )       ), "numbers": datasets.Value("float32"       )}       )
          a_       		=generate_example_dataset(
              os.path.join(lowercase__       ,	"dataset.arrow"       )       ,	lowercase__       ,	num_examples=lowercase__       ,	seq_shapes={"list": (1_0_0,)}       ,	)
          print("first set of iterations"       )
          for func, kwargs in functions:
               print(func.__name__       ,	str(lowercase__       )       )
               a_       		=func(lowercase__       ,	**lowercase__       )

          print("shuffling dataset"       )
          a_       		=dataset.shuffle()
          print("Second set of iterations (after shuffling"       )
          for func, kwargs in functions_shuffled:
               print("shuffled "       ,	func.__name__       ,	str(lowercase__       )       )
               a_       		=func(
                   lowercase__       ,	**lowercase__       )

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


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







'''simple docstring'''


from typing import Any

import numpy as np






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     return np.array_equal(lowercase__       ,	matrix.conjugate().T       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=v.conjugate().T
     a_       		=v_star.dot(lowercase__       )
     assert isinstance(lowercase__       ,	np.ndarray       )
     return (v_star_dot.dot(lowercase__       )) / (v_star.dot(lowercase__       ))






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]       )
     a_       		=np.array([[1], [2], [3]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     print(rayleigh_quotient(lowercase__       ,	lowercase__       )       )

     a_       		=np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     assert rayleigh_quotient(lowercase__       ,	lowercase__       ) == float(3       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()


