#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

- case: sampling
  main: |
    from pyspark.sql import SparkSession

    spark = SparkSession.builder.getOrCreate()
    df = spark.range(1)
    df.sample(1.0)
    df.sample(0.5, 3)
    df.sample(fraction=0.5, seed=3)
    df.sample(withReplacement=True, fraction=0.5, seed=3)
    df.sample(fraction=1.0)
    df.sample(False, fraction=1.0)

    # Will raise a runtime error, though not a typecheck error, as bool is
    # duck-type compatible with float.
    df.sample(True)

    df.sample(withReplacement=False)

  out: |
    main:16: error: No overload variant of "sample" of "DataFrame" matches argument type "bool"  [call-overload]
    main:16: note: Possible overload variants:
    main:16: note:     def sample(self, fraction: float, seed: Optional[int] = ...) -> DataFrame
    main:16: note:     def sample(self, withReplacement: Optional[bool], fraction: float, seed: Optional[int] = ...) -> DataFrame


- case: selectColumns
  main: |
    from pyspark.sql import SparkSession
    from pyspark.sql.functions import col

    spark = SparkSession.builder.getOrCreate()

    data = [('Alice', 1)]
    df = spark.createDataFrame(data, schema="name str, age int")

    df.select(["name", "age"])
    df.select([col("name"), col("age")])

    df.select(["name", col("age")])  # E: Argument 1 to "select" of "DataFrame" has incompatible type "List[object]"; expected "Union[List[Column], List[str]]"  [arg-type]


- case: groupBy
  main: |
    from pyspark.sql import SparkSession
    from pyspark.sql.functions import col

    spark = SparkSession.builder.getOrCreate()

    data = [('Alice', 1)]
    df = spark.createDataFrame(data, schema="name str, age int")

    df.groupBy(["name", "age"])
    df.groupby(["name", "age"])
    df.groupBy([col("name"), col("age")])
    df.groupby([col("name"), col("age")])
    df.groupBy(["name", col("age")])  # E: Argument 1 to "groupBy" of "DataFrame" has incompatible type "List[object]"; expected "Union[List[Column], List[str]]"  [arg-type]


- case: rollup
  main: |
    from pyspark.sql import SparkSession
    from pyspark.sql.functions import col

    spark = SparkSession.builder.getOrCreate()

    data = [('Alice', 1)]
    df = spark.createDataFrame(data, schema="name str, age int")

    df.rollup(["name", "age"])
    df.rollup([col("name"), col("age")])


    df.rollup(["name", col("age")])  # E: Argument 1 to "rollup" of "DataFrame" has incompatible type "List[object]"; expected "Union[List[Column], List[str]]"  [arg-type]


- case: cube
  main: |
    from pyspark.sql import SparkSession
    from pyspark.sql.functions import col

    spark = SparkSession.builder.getOrCreate()

    data = [('Alice', 1)]
    df = spark.createDataFrame(data, schema="name str, age int")

    df.cube(["name", "age"])
    df.cube([col("name"), col("age")])


    df.cube(["name", col("age")])  # E: Argument 1 to "cube" of "DataFrame" has incompatible type "List[object]"; expected "Union[List[Column], List[str]]"  [arg-type]


- case: dropColumns
  main: |
    from pyspark.sql import SparkSession
    from pyspark.sql.functions import lit, col

    spark = SparkSession.builder.getOrCreate()
    df = spark.range(1)
    df.drop("id")
    df.drop("id", "foo")
    df.drop(df.id)

    df.drop(col("id"), col("foo"))

  out: |
    main:10: error: No overload variant of "drop" of "DataFrame" matches argument types "Column", "Column"  [call-overload]
    main:10: note: Possible overload variants:
    main:10: note:     def drop(self, cols: Union[Column, str]) -> DataFrame
    main:10: note:     def drop(self, *cols: str) -> DataFrame


- case: fillNullValues
  main: |
    from pyspark.sql import SparkSession

    spark = SparkSession.builder.getOrCreate()
    df = spark.createDataFrame([(1,2)], schema=("id1", "id2"))

    df.fillna(value=1, subset="id1")
    df.fillna(value=1, subset=("id1", "id2"))
    df.fillna(value=1, subset=["id1"])