
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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       lowercase							:					List[str]	     =				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}}"""    )
       lowercase							:					int	     =				DatasetInfosDict.from_directory(SCREAMING_SNAKE_CASE__    )
       assert dataset_infos
       assert dataset_infos["default"].dataset_size == 42







@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=42 ,     ),
    ] ,     )
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[str]:
       lowercase							:					Optional[Any]	     =				str(SCREAMING_SNAKE_CASE__    )
       dataset_info.write_to_directory(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Tuple	     =				DatasetInfo.from_directory(SCREAMING_SNAKE_CASE__    )
       assert dataset_info == reloaded
       assert os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__ ,     """dataset_info.json"""    )    )







def 						_snake_case(  )			->   Optional[Any]:
       lowercase							:					Dict	     =				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""": 42}] ,     download_checksums={} ,     download_size=1_337 ,     post_processing_size=442 ,     dataset_size=1_234 ,     size_in_bytes=1_337 + 442 + 1_234 ,     )
       lowercase							:					Optional[Any]	     =				dataset_info._to_yaml_dict()
       assert sorted(SCREAMING_SNAKE_CASE__    ) == 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)    )
       lowercase							:					Dict	     =				yaml.safe_dump(SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[str]	     =				yaml.safe_load(SCREAMING_SNAKE_CASE__    )
       assert dataset_info_yaml_dict == reloaded







def 						_snake_case(  )			->   List[str]:
       lowercase							:					List[Any]	     =				DatasetInfo()
       lowercase							:					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=42 ,     )
            }    ),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42    ),
                """v2""": DatasetInfo(dataset_size=1_337    ),
            }    ),
    ] ,     )
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[Any]:
       lowercase							:					List[str]	     =				str(SCREAMING_SNAKE_CASE__    )
       dataset_infos_dict.write_to_directory(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Union[str, Any]	     =				DatasetInfosDict.from_directory(SCREAMING_SNAKE_CASE__    )

       # the config_name of the dataset_infos_dict take over the attribute
       for config_name, dataset_info in dataset_infos_dict.items():
              lowercase							:					Optional[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
              lowercase							:					Optional[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(SCREAMING_SNAKE_CASE__ ,     """README.md"""    )    )


def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       global f  # a global dp table for knapsack
       if f[i][j] < 0:
              if j < wt[i - 1]:
                     lowercase							:					str	     =				mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
              else:
                     lowercase							:					Optional[int]	     =				max(
                         mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) ,     mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     j - wt[i - 1]    ) + val[i - 1] ,     )
              lowercase							:					List[Any]	     =				val
       return f[i][j]







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Any:
       lowercase							:					Optional[int]	     =				[[0] * (w + 1) for _ in range(n + 1    )]

       for i in range(1 ,     n + 1    ):
              for w_ in range(1 ,     w + 1    ):
                     if wt[i - 1] <= w_:
                            lowercase							:					Union[str, Any]	     =				max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,     dp[i - 1][w_]    )
                     else:
                            lowercase							:					Any	     =				dp[i - 1][w_]

       return dp[n][w_], dp







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       if not (isinstance(SCREAMING_SNAKE_CASE__ ,     (list, tuple)    ) and isinstance(SCREAMING_SNAKE_CASE__ ,     (list, tuple)    )):
              raise ValueError(
                  """Both the weights and values vectors must be either lists or tuples"""    )

       lowercase							:					Optional[Any]	     =				len(SCREAMING_SNAKE_CASE__    )
       if num_items != len(SCREAMING_SNAKE_CASE__    ):
              lowercase							:					Optional[Any]	     =				(
                  """The number of weights must be the same as the number of values.\n"""
                  f"But got {num_items} weights and {len(SCREAMING_SNAKE_CASE__    )} values"
              )
              raise ValueError(SCREAMING_SNAKE_CASE__    )
       for i in range(SCREAMING_SNAKE_CASE__    ):
              if not isinstance(wt[i] ,     SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Tuple	     =				(
                         """All weights must be integers but got weight of """
                         f"type {type(wt[i]    )} at index {i}"
                     )
                     raise TypeError(SCREAMING_SNAKE_CASE__    )

       lowercase     ,      lowercase							:					Any	     =				knapsack(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       lowercase							:					set	     =				set()
       _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       return optimal_val, example_optional_set







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       # for the current item i at a maximum weight j to be part of an optimal subset,
       # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
       # where i - 1 means considering only the previous items at the given maximum weight
       if i > 0 and j > 0:
              if dp[i - 1][j] == dp[i][j]:
                     _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
              else:
                     optimal_set.add(SCREAMING_SNAKE_CASE__    )
                     _construct_solution(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     i - 1 ,     j - wt[i - 1] ,     SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
  lowercase  : Dict						   =							[3, 2, 4, 4]
  lowercase  : List[Any]						   =							[4, 3, 2, 3]
  lowercase  : Tuple						   =							4
  lowercase  : Tuple						   =							6
  lowercase  : Tuple						   =							[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
  lowercase   ,  lowercase  : List[str]						   =							knapsack(w, wt, val, n)
  print(optimal_solution)
  print(mf_knapsack(n, wt, val, w))  # switched the n and w

  # testing the dynamic programming problem with example
  # the optimal subset for the above example are items 3 and 4
  lowercase   ,  lowercase  : Optional[int]						   =							knapsack_with_example_solution(w, wt, val)
  assert optimal_solution == 8
  assert optimal_subset == {3, 4}
  print("""optimal_value = """, optimal_solution)
  print("""An optimal subset corresponding to the optimal value""", optimal_subset)
