Error message is not clear when local file is missing

Here's the code to repro:

For the code below, put in the right value for `DATABRICKS_WORKSPACE_TOKEN_VALUE`. Then run the whole code.

```

from feathr import AvroJsonSchema
from feathr import KafKaSource
from feathr import KafkaConfig
from typing import List
import os
import random
from datetime import datetime, timedelta

from feathr import (BOOLEAN, FLOAT, INPUT_CONTEXT, INT32, STRING,
DerivedFeature, Feature, FeatureAnchor, HdfsSource,
TypedKey, ValueType, WindowAggTransformation)
from feathr import FeathrClient
from pyspark.sql import DataFrame
import os
from pathlib import Path
import pytest
from feathr import MaterializationSettings, RedisSink
import tempfile

yaml_config = """
# DO NOT MOVE OR DELETE THIS FILE

# This file contains the configurations that are used by Feathr
# All the configurations can be overwritten by environment variables with concatenation of `__` for different layers of this config file.
# For example, `feathr_runtime_location` for databricks can be overwritten by setting this environment variable:
# SPARK_CONFIG__DATABRICKS__FEATHR_RUNTIME_LOCATION
# Another example would be overwriting Redis host with this config: `ONLINE_STORE__REDIS__HOST`
# For example if you want to override this setting in a shell environment:
# export ONLINE_STORE__REDIS__HOST=feathrazure.redis.cache.windows.net

# version of API settings
api_version: 1
project_config:
project_name: 'project_feathr_integration_test'
# Information that are required to be set via environment variables.
required_environment_variables:
# the environemnt variables are required to run Feathr
# Redis password for your online store
- 'REDIS_PASSWORD'
# client IDs and client Secret for the service principal. Read the getting started docs on how to get those information.
- 'AZURE_CLIENT_ID'
- 'AZURE_TENANT_ID'
- 'AZURE_CLIENT_SECRET'
optional_environment_variables:
# the environemnt variables are optional, however you will need them if you want to use some of the services:
- ADLS_ACCOUNT
- ADLS_KEY
- WASB_ACCOUNT
- WASB_KEY
- S3_ACCESS_KEY
- S3_SECRET_KEY
- JDBC_TABLE
- JDBC_USER
- JDBC_PASSWORD
- KAFKA_SASL_JAAS_CONFIG

offline_store:
# paths starts with abfss:// or abfs://
# ADLS_ACCOUNT and ADLS_KEY should be set in environment variable if this is set to true
adls:
adls_enabled: true

# paths starts with wasb:// or wasbs://
# WASB_ACCOUNT and WASB_KEY should be set in environment variable
wasb:
wasb_enabled: true

# paths starts with s3a://
# S3_ACCESS_KEY and S3_SECRET_KEY should be set in environment variable
s3:
s3_enabled: true
# S3 endpoint. If you use S3 endpoint, then you need to provide access key and secret key in the environment variable as well.
s3_endpoint: 's3.amazonaws.com'

# jdbc endpoint
jdbc:
jdbc_enabled: true
jdbc_database: 'feathrtestdb'
jdbc_table: 'feathrtesttable'

# snowflake endpoint
snowflake:
url: "dqllago-ol19457.snowflakecomputing.com"
user: "feathrintegration"
role: "ACCOUNTADMIN"

spark_config:
# choice for spark runtime. Currently support: azure_synapse, databricks
# The `databricks` configs will be ignored if `azure_synapse` is set and vice versa.
spark_cluster: 'databricks'
# configure number of parts for the spark output for feature generation job
spark_result_output_parts: '1'

azure_synapse:
dev_url: 'https://feathrazuretest3synapse.dev.azuresynapse.net'
pool_name: 'spark3'
# workspace dir for storing all the required configuration files and the jar resources
workspace_dir: 'abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/feathr_test_workspace'
executor_size: 'Small'
executor_num: 4
# Feathr Job configuration. Support local paths, path start with http(s)://, and paths start with abfs(s)://
# this is the default location so end users don't have to compile the runtime again.
# feathr_runtime_location: wasbs://public@azurefeathrstorage.blob.core.windows.net/feathr-assembly-0.5.0-SNAPSHOT.jar
feathr_runtime_location: "../../target/scala-2.12/feathr-assembly-0.5.0.jar"
databricks:
# workspace instance
workspace_instance_url: 'https://adb-2474129336842816.16.azuredatabricks.net/'
workspace_token_value: ''
# config string including run time information, spark version, machine size, etc.
# the config follows the format in the databricks documentation: https://docs.microsoft.com/en-us/azure/databricks/dev-tools/api/2.0/jobs
config_template: {"run_name":"FEATHR_FILL_IN","new_cluster":{"spark_version":"9.1.x-scala2.12","num_workers":2,"spark_conf":{"FEATHR_FILL_IN":"FEATHR_FILL_IN"},"instance_pool_id":"0403-214809-inlet434-pool-l9dj3kwz"},"libraries":[{"jar":"FEATHR_FILL_IN"}],"spark_jar_task":{"main_class_name":"FEATHR_FILL_IN","parameters":["FEATHR_FILL_IN"]}}
# Feathr Job location. Support local paths, path start with http(s)://, and paths start with dbfs:/
work_dir: 'dbfs:/feathr_getting_started'
# this is the default location so end users don't have to compile the runtime again.
feathr_runtime_location: "../../target/scala-2.12/feathr-assembly-0.5.0.jar"

online_store:
redis:
# Redis configs to access Redis cluster
host: 'feathrazuretest3redis.redis.cache.windows.net'
port: 6380
ssl_enabled: True

feature_registry:
# The API endpoint of the registry service
api_endpoint: "https://feathr-sql-registry.azurewebsites.net/api/v1"

monitoring:
database:
sql:
url: 'jdbc:postgresql://featuremonitoring.postgres.database.azure.com:5432/postgres'
user: "demo"

"""

import os
os.environ['DATABRICKS_WORKSPACE_TOKEN_VALUE'] = ''

# write this configuration string to a temporary location and load it to Feathr
tmp = tempfile.NamedTemporaryFile(mode='w', delete=False)
with open(tmp.name, "w") as text_file:
text_file.write(yaml_config)

client = FeathrClient(config_path=tmp.name)
schema = AvroJsonSchema(schemaStr="""
{
"type": "record",
"name": "DriverTrips",
"fields": [
{"name": "driver_id", "type": "long"},
{"name": "trips_today", "type": "int"},
{
"name": "datetime",
"type": {"type": "long", "logicalType": "timestamp-micros"}
}
]
}
""")
stream_source = KafKaSource(name="kafkaStreamingSource",
kafkaConfig=KafkaConfig(brokers=["feathrazureci.servicebus.windows.net:9093"],
topics=["feathrcieventhub"],
schema=schema)
)

driver_id = TypedKey(key_column="driver_id",
key_column_type=ValueType.INT64,
description="driver id",
full_name="nyc driver id")

kafkaAnchor = FeatureAnchor(name="kafkaAnchor",
source=stream_source,
features=[Feature(name="f_modified_streaming_count",
feature_type=INT32,
transform="trips_today + 1",
key=driver_id),
Feature(name="f_modified_streaming_count2",
feature_type=INT32,
transform="trips_today + 2",
key=driver_id)]
)
client.build_features(anchor_list=[kafkaAnchor])

redisSink = RedisSink(table_name="kafkaSampleDemoFeature", streaming=True, streamingTimeoutMs=10000)
settings = MaterializationSettings(name="kafkaSampleDemo",
sinks=[redisSink],
feature_names=['f_modified_streaming_count']
)
client.materialize_features(settings)
client.wait_job_to_finish(timeout_sec=600)

```

The error will be something like:

```
raceback (most recent call last):
File "/opt/miniconda3/lib/python3.9/site-packages/click/globals.py", line 37, in get_current_context
return t.cast("Context", _local.stack[-1])
AttributeError: '_thread._local' object has no attribute 'stack'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/Users/xiazhu/Desktop/kafka_streaming_test.py", line 198, in
client.materialize_features(settings)
File "/Users/xiazhu/Documents/GitHub/feathr/feathr_project/feathr/client.py", line 566, in materialize_features
self._materialize_features_with_config(config_file_path, execution_configurations, udf_files)
File "/Users/xiazhu/Documents/GitHub/feathr/feathr_project/feathr/client.py", line 613, in _materialize_features_with_config
return self.feathr_spark_launcher.submit_feathr_job(
File "/Users/xiazhu/Documents/GitHub/feathr/feathr_project/feathr/spark_provider/_databricks_submission.py", line 158, in submit_feathr_job
submission_params['libraries'][0]['jar'] = self.upload_or_get_cloud_path(main_jar_path)
File "/Users/xiazhu/Documents/GitHub/feathr/feathr_project/feathr/spark_provider/_databricks_submission.py", line 104, in upload_or_get_cloud_path
returned_path = self.upload_local_file(local_path_or_http_path)
File "/Users/xiazhu/Documents/GitHub/feathr/feathr_project/feathr/spark_provider/_databricks_submission.py", line 116, in upload_local_file
DbfsApi(self.api_client).cp(recursive=True, overwrite=True, src=local_path, dst=returned_path)
File "/opt/miniconda3/lib/python3.9/site-packages/databricks_cli/dbfs/api.py", line 264, in cp
error_and_quit('The local file {} does not exist.'.format(src))
File "/opt/miniconda3/lib/python3.9/site-packages/databricks_cli/utils.py", line 90, in error_and_quit
ctx = click.get_current_context()
File "/opt/miniconda3/lib/python3.9/site-packages/click/globals.py", line 40, in get_current_context
raise RuntimeError("There is no active click context.") from e
RuntimeError: There is no active click context.
```

The root cause is because this line in the error message:

```
error_and_quit('The local file {} does not exist.'.format(src))
```

and
```
../../target/scala-2.12/feathr-assembly-0.5.0.jar
```
this file doesn't exist.

We should improve the message, either by adding try catch, or expose the stacktrace by DbfsAPI.