
"""simple docstring"""
import math





def    _lowerCAmelCase    (     lowerCamelCase__							:							float,						lowerCamelCase__							:							float				)  ->    float:

  if initial_intensity < 0:
    raise ValueError("The value of intensity cannot be negative"				)
    # handling of negative values of initial intensity
  if angle < 0 or angle > 3_6_0:
    raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"				)
    # handling of values out of allowed range
  return initial_intensity * (math.cos(math.radians(lowerCamelCase__				)				) ** 2)


if __name__ == "__main__":
      import doctest

      doctest.testmod(name='''malus_law''')





"""simple docstring"""
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    "files",						[
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tmp_path_factory.mktemp("dset_infos_dir"				)
  if "full:README.md" in files:
    with open(dataset_infos_dir / "README.md",						"w"				) as f:
      f.write("---\ndataset_info:\n  dataset_size: 42\n---"				)
  if "empty:README.md" in files:
    with open(dataset_infos_dir / "README.md",						"w"				) as f:
      f.write(""				)
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / "dataset_infos.json",						"w"				) as f:
      f.write("{\"default\": {\"dataset_size\": 42}}"				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = DatasetInfosDict.from_directory(lowerCamelCase__				)
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 4_2





@pytest.mark.parametrize(
    "dataset_info",						[
        DatasetInfo(),
        DatasetInfo(
            description="foo",						features=Features({"a": Value("int32"				)}				),						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train"}],						download_size=4_2,						),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							DatasetInfo				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = str(lowerCamelCase__				)
  dataset_info.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				int							    = DatasetInfo.from_directory(lowerCamelCase__				)
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(lowerCamelCase__,						"dataset_info.json"				)				)





def    _lowerCAmelCase    (     )  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				str							    = DatasetInfo(
      description="foo",						citation="bar",						homepage="https://foo.bar",						license="CC0",						features=Features({"a": Value("int32"				)}				),						post_processed={},						supervised_keys=(),						task_templates=[],						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train", "num_examples": 4_2}],						download_checksums={},						download_size=1_3_3_7,						post_processing_size=4_4_2,						dataset_size=1_2_3_4,						size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4,						)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = dataset_info._to_yaml_dict()
  assert sorted(lowerCamelCase__				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key],						(list, dict, int, str)				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = yaml.safe_dump(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = yaml.safe_load(lowerCamelCase__				)
  assert dataset_info_yaml_dict == reloaded





def    _lowerCAmelCase    (     )  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				Dict							    = DatasetInfo()
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    "dataset_infos_dict",						[
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}				),
        DatasetInfosDict({"my_config_name": DatasetInfo()}				),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo",						features=Features({"a": Value("int32"				)}				),						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train"}],						download_size=4_2,						)
            }				),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=4_2				),
                "v2": DatasetInfo(dataset_size=1_3_3_7				),
            }				),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							DatasetInfosDict				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				str							    = str(lowerCamelCase__				)
  dataset_infos_dict.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = DatasetInfosDict.from_directory(lowerCamelCase__				)

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    _SCREAMING_SNAKE_CASE   :				Dict							    = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(lowerCamelCase__,						"README.md"				)				)



