remove native snowpark cortex func tests

Signed-off-by: Labanya Mukhopadhyay <[email protected]>

docs/source/snowpark/functions.rst

@@ -296,8 +296,6 @@ Functions
     sinh
     size
     skew
-    snowflake_cortex_sentiment
-    snowflake_cortex_summarize
     sort_array
     soundex
     split

tests/integ/test_function.py

@@ -128,8 +128,6 @@
     reverse,
     sequence,
     size,
-    snowflake_cortex_sentiment,
-    snowflake_cortex_summarize,
     split,
     sqrt,
     startswith,
@@ -179,7 +177,6 @@
 from tests.utils import (
     TestData,
     Utils,
-    running_on_jenkins,
     structured_types_enabled_session,
 )
 
@@ -2329,70 +2326,3 @@ def test_ln(session):
     df = session.create_dataframe([[e]], schema=["ln_value"])
     res = df.select(ln(col("ln_value")).alias("result")).collect()
     assert res[0][0] == 1.0
-
-
-@pytest.mark.skipif(
-    "config.getoption('local_testing_mode', default=False)",
-    reason="FEAT: snowflake_cortex functions not supported",
-)
-@pytest.mark.skipif(
-    running_on_jenkins(),
-    reason="TODO: SNOW-1859087 snowflake.cortex.summarize SSL error",
-)
-def test_snowflake_cortex_summarize(session):
-    # TODO: SNOW-1758914 snowflake.cortex.summarize error on GCP
-    if session.connection.host == "sfctest0.us-central1.gcp.snowflakecomputing.com":
-        return
-
-    content = """In Snowpark, the main way in which you query and process data is through a DataFrame. This topic explains how to work with DataFrames.
-
-To retrieve and manipulate data, you use the DataFrame class. A DataFrame represents a relational dataset that is evaluated lazily: it only executes when a specific action is triggered. In a sense, a DataFrame is like a query that needs to be evaluated in order to retrieve data.
-
-To retrieve data into a DataFrame:
-
-Construct a DataFrame, specifying the source of the data for the dataset.
-
-For example, you can create a DataFrame to hold data from a table, an external CSV file, from local data, or the execution of a SQL statement.
-
-Specify how the dataset in the DataFrame should be transformed.
-
-For example, you can specify which columns should be selected, how the rows should be filtered, how the results should be sorted and grouped, etc.
-
-Execute the statement to retrieve the data into the DataFrame.
-
-In order to retrieve the data into the DataFrame, you must invoke a method that performs an action (for example, the collect() method).
-
-The next sections explain these steps in more detail.
-"""
-    df = session.create_dataframe([[content]], schema=["content"])
-    summary_from_col = df.select(snowflake_cortex_summarize(col("content"))).collect()[
-        0
-    ][0]
-    summary_from_str = df.select(snowflake_cortex_summarize(content)).collect()[0][0]
-    # this length check is to get around the fact that this function may not be deterministic
-    assert 0 < len(summary_from_col) < len(content)
-    assert 0 < len(summary_from_str) < len(content)
-
-
-@pytest.mark.skipif(
-    "config.getoption('local_testing_mode', default=False)",
-    reason="FEAT: snowflake_cortex functions not supported",
-)
-@pytest.mark.skipif(
-    running_on_jenkins(),
-    reason="TODO: SNOW-1859087 snowflake.cortex.sentiment SSL error",
-)
-def test_snowflake_cortex_sentiment(session):
-    # TODO: SNOW-1758914 snowflake.cortex.sentiment error on GCP
-    if session.connection.host == "sfctest0.us-central1.gcp.snowflakecomputing.com":
-        return
-    content = "A very very bad review!"
-    df = session.create_dataframe([[content]], schema=["content"])
-
-    sentiment_from_col = df.select(
-        snowflake_cortex_sentiment(col("content"))
-    ).collect()[0][0]
-    sentiment_from_str = df.select(snowflake_cortex_sentiment(content)).collect()[0][0]
-
-    assert -1 <= sentiment_from_col <= 0
-    assert -1 <= sentiment_from_str <= 0