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class torch:
@staticmethod
def ones(*args):
return torch
@staticmethod
def long():
return torch
@staticmethod
def to(device: str):
return torch.Tensor()
class Tensor:
pass
def transform(x):
return x
class spark:
class read:
@staticmethod
def parquet(file_name: str):
return spark.DataFrame()
class functions:
@staticmethod
def lit(constant):
return constant
@staticmethod
def col(col_name):
return col_name
class DataFrame:
@staticmethod
def withColumnRenamed(col_in, col_out):
return spark.DataFrame()
@staticmethod
def withColumn(col_in, col_out):
return spark.DataFrame()
@staticmethod
def select(*args):
return spark.DataFrame()
class F:
@staticmethod
def lit(value):
return value
# these will match
def get_tensors(device: str) -> torch.Tensor:
a = torch.ones(2, 1)
a = a.long()
a = a.to(device)
return a
def process(file_name: str):
common_columns = ["col1_renamed", "col2_renamed", "custom_col"]
df = spark.read.parquet(file_name)
df = df \
.withColumnRenamed('col1', 'col1_renamed') \
.withColumnRenamed('col2', 'col2_renamed')
df = df \
.select(common_columns) \
.withColumn('service_type', spark.functions.lit('green'))
return df
def projection(df_in: spark.DataFrame) -> spark.DataFrame:
df = (
df_in.select(["col1", "col2"])
.withColumnRenamed("col1", "col1a")
)
return df.withColumn("col2a", spark.functions.col("col2").cast("date"))
def assign_multiple(df):
df = df.select("column")
result_df = df.select("another_column")
final_df = result_df.withColumn("column2", F.lit("abc"))
return final_df
# not yet supported
def assign_alternating(df, df2):
df = df.select("column")
df2 = df2.select("another_column")
df = df.withColumn("column2", F.lit("abc"))
return df, df2
# these will not
def ignored(x):
_ = x.op1()
_ = _.op2()
return _
def _(x):
y = x.m()
return y.operation(*[v for v in y])
def assign_multiple_referenced(df, df2):
df = df.select("column")
result_df = df.select("another_column")
return df, result_df
def invalid(df_in: spark.DataFrame, alternative_df: spark.DataFrame) -> spark.DataFrame:
df = (
df_in.select(["col1", "col2"])
.withColumnRenamed("col1", "col1a")
)
return alternative_df.withColumn("col2a", spark.functions.col("col2").cast("date"))
def no_match():
y = 10
y = transform(y)
return y
def f(x):
if x:
name = "alice"
stripped = name.strip()
print(stripped)
else:
name = "bob"
print(name)
def g(x):
try:
name = "alice"
stripped = name.strip()
print(stripped)
except ValueError:
name = "bob"
print(name)
def h(x):
for _ in (1, 2, 3):
name = "alice"
stripped = name.strip()
print(stripped)
else:
name = "bob"
print(name)
def assign_multiple_try(df):
try:
df = df.select("column")
result_df = df.select("another_column")
final_df = result_df.withColumn("column2", F.lit("abc"))
return final_df
except ValueError:
return None
|
