
"""simple docstring"""




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A__   :					Dict							=   5_0_0_0_0_0

A__							, A__   :					Dict							=   os.path.split(__file__)
A__   :					Optional[int]							=   os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def  _lowerCAmelCase		(		_UpperCamelCase ,				**_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[int]       =     dataset.map(**_UpperCamelCase						)




@get_duration
def  _lowerCAmelCase		(		_UpperCamelCase ,				**_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[int]       =     dataset.filter(**_UpperCamelCase						)




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			str       =     {'''num examples''': SPEED_TEST_N_EXAMPLES}
  with tempfile.TemporaryDirectory() as tmp_dir:
    _lowercase:			Any       =     datasets.Features({'''text''': datasets.Value('''string'''						), '''numbers''': datasets.Value('''float32'''						)}						)
    _lowercase:			Dict       =     generate_example_dataset(
        os.path.join(_UpperCamelCase ,				'''dataset.arrow'''						) ,				_UpperCamelCase ,				num_examples=_UpperCamelCase						)

    _lowercase:			Optional[int]       =     transformers.AutoTokenizer.from_pretrained('''bert-base-cased''' ,				use_fast=_UpperCamelCase						)

    def tokenize(_UpperCamelCase						):
      return tokenizer(examples['''text''']						)

    _lowercase:			Optional[Any]       =     map(_UpperCamelCase						)

    _lowercase:			str       =     map(_UpperCamelCase ,				batched=_UpperCamelCase						)

    _lowercase:			Tuple       =     map(_UpperCamelCase ,				function=lambda _UpperCamelCase						: None ,				batched=_UpperCamelCase						)

    with dataset.formatted_as(type='''numpy'''						):
      _lowercase:			Any       =     map(_UpperCamelCase ,				function=lambda _UpperCamelCase						: None ,				batched=_UpperCamelCase						)

    with dataset.formatted_as(type='''pandas'''						):
      _lowercase:			int       =     map(_UpperCamelCase ,				function=lambda _UpperCamelCase						: None ,				batched=_UpperCamelCase						)

    with dataset.formatted_as(type='''torch''' ,				columns='''numbers'''						):
      _lowercase:			List[str]       =     map(_UpperCamelCase ,				function=lambda _UpperCamelCase						: None ,				batched=_UpperCamelCase						)

    with dataset.formatted_as(type='''tensorflow''' ,				columns='''numbers'''						):
      _lowercase:			Dict       =     map(_UpperCamelCase ,				function=lambda _UpperCamelCase						: None ,				batched=_UpperCamelCase						)

    _lowercase:			Any       =     map(_UpperCamelCase ,				function=_UpperCamelCase ,				batched=_UpperCamelCase						)

    _lowercase:			str       =     filter(_UpperCamelCase						)

    # 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(_UpperCamelCase ,				'''wb'''						) as f:
    f.write(json.dumps(_UpperCamelCase						).encode('''utf-8'''						)						)


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


"""simple docstring"""




from typing import Any

import numpy as np




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			str       =     v.conjugate().T
  _lowercase:			List[Any]       =     v_star.dot(_UpperCamelCase						)
  assert isinstance(_UpperCamelCase ,				np.ndarray						)
  return (v_star_dot.dot(_UpperCamelCase						)) / (v_star.dot(_UpperCamelCase						))




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			int       =     np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]						)
  _lowercase:			str       =     np.array([[1], [2], [3]]						)
  assert is_hermitian(_UpperCamelCase						), f'''{a} is not hermitian.'''
  print(rayleigh_quotient(_UpperCamelCase ,				_UpperCamelCase						)						)

  _lowercase:			Any       =     np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]						)
  assert is_hermitian(_UpperCamelCase						), f'''{a} is not hermitian.'''
  assert rayleigh_quotient(_UpperCamelCase ,				_UpperCamelCase						) == float(3						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()
