
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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   List[Any]					=				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}}'''		)
      UpperCamelCase__    :   Dict					=				DatasetInfosDict.from_directory(UpperCamelCase__		)
      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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Optional[int]					=				str(UpperCamelCase__		)
      dataset_info.write_to_directory(UpperCamelCase__		)
      UpperCamelCase__    :   Optional[Any]					=				DatasetInfo.from_directory(UpperCamelCase__		)
      assert dataset_info == reloaded
      assert os.path.exists(os.path.join(UpperCamelCase__ ,		'''dataset_info.json'''		)		)


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   Optional[Any]					=				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 ,		)
      UpperCamelCase__    :   List[Any]					=				dataset_info._to_yaml_dict()
      assert sorted(UpperCamelCase__		) == 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)		)
      UpperCamelCase__    :   List[str]					=				yaml.safe_dump(UpperCamelCase__		)
      UpperCamelCase__    :   Union[str, Any]					=				yaml.safe_load(UpperCamelCase__		)
      assert dataset_info_yaml_dict == reloaded


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   List[Any]					=				DatasetInfo()
      UpperCamelCase__    :   Optional[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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Dict					=				str(UpperCamelCase__		)
      dataset_infos_dict.write_to_directory(UpperCamelCase__		)
      UpperCamelCase__    :   List[Any]					=				DatasetInfosDict.from_directory(UpperCamelCase__		)

      # the config_name of the dataset_infos_dict take over the attribute
      for config_name, dataset_info in dataset_infos_dict.items():
            UpperCamelCase__    :   Union[str, 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
            UpperCamelCase__    :   int					=				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(UpperCamelCase__ ,		'''README.md'''		)		)




from __future__ import annotations

from collections.abc import Callable


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = 1_0_0 ,		):
      UpperCamelCase__    :   Union[str, Any]					=				x_start
      UpperCamelCase__    :   List[Any]					=				fnc(UpperCamelCase__		)
      UpperCamelCase__    :   Any					=				0.0
      for _ in range(UpperCamelCase__		):
            # Approximates small segments of curve as linear and solve
            # for trapezoidal area
            UpperCamelCase__    :   str					=				(x_end - x_start) / steps + xa
            UpperCamelCase__    :   Dict					=				fnc(UpperCamelCase__		)
            area += abs(fxa + fxa		) * (xa - xa) / 2
            # Increment step
            UpperCamelCase__    :   Tuple					=				xa
            UpperCamelCase__    :   Union[str, Any]					=				fxa
      return area


if __name__ == "__main__":


    def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
          return x**3 + x**2

    print("f(x) = x^3 + x^2")
    print("The area between the curve, x = -5, x = 5 and the x axis is:")
    lowerCamelCase      =1_0
    while i <= 1_0_0_0_0_0:
        print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
        i *= 1_0


