Task: NL2SQL (source dataset: Spider)

Natural language to SQL, also known as NL2SQL, is the problem of generating a SQL query that can be executed on a input table, for a given natural language question.

You will be given an input table called "table" in the markdown format, as well as a question. Your task is to generate an SQL query that can be executed on the input table to answer the question.

Please ensure that the generated SQL only return relevant columns being specifically asked in the question (e.g., in SQL please don't return more than one columns in SELECT if only one column is needed to answer the given question, and similarly please refrain from using SELECT * unless the entire row is needed to answer the question).
 
When generating SQL code, please use the SQLite syntax to ensure that your SQL is executable on SQLite3. Use only columns that are present in the table, always use quotes around your column and table names (some column names have punctuation or special characters), and refer to the columns using the column-names exactly as they appear in the table, do not add or remove punctuation (underscore, slash, etc.)

No explanation, return the code only with the following markdown codeblock format : ```sql<SQL CODE>```. For example, ```sql
SELECT * FROM "table"
``` is a valid format.

Database:
government_shift

Tables:

Table: Services
|   Service_ID | Service_Details   |
|-------------:|:------------------|
|          313 | driving license   |
|          430 | broker license    |
|          457 | dog license       |
|          575 | building permit   |
|          606 | liquor license    |
|          620 | library card      |
|          828 | working permit    |


Table: Customers
|   Customer_ID | Customer_Details         |
|--------------:|:-------------------------|
|            12 | Amalia Johnston          |
|            32 | Miss Annamarie Lowe      |
|            78 | Miss Alexandra Kemmer MD |
|            93 | Agustina Stoltenberg     |
|            98 | Dr. Jessyca Roob         |
|           103 | Hardy Kutch              |
|           113 | Vicky Keeling            |
|           119 | Verdie Stehr             |
|           173 | Dr. Rupert Lind          |
|           212 | Flo Crooks               |


Table: Channels
|   Channel_ID | Channel_Details   |
|-------------:|:------------------|
|           16 | 15 ij             |
|           45 | 75 ww             |
|           55 | 92 ve             |
|           65 | 40 zy             |
|           78 | 13 ik             |


Table: Customers_and_Services
|   Customers_and_Services_ID |   Customer_ID |   Service_ID | Customers_and_Services_Details   |
|----------------------------:|--------------:|-------------:|:---------------------------------|
|                         123 |            12 |          313 | Satisfied                        |
|                         130 |            12 |          620 | Satisfied                        |
|                         203 |            93 |          828 | Satisfied                        |
|                         228 |           286 |          430 | Satisfied                        |
|                         350 |           113 |          313 | Satisfied                        |
|                         420 |           113 |          575 | Satisfied                        |
|                         428 |           103 |          575 | Unsatisfied                      |
|                         465 |           217 |          457 | Unsatisfied                      |
|                         475 |            78 |          575 | Unsatisfied                      |
|                         606 |           256 |          828 | Unsatisfied                      |


Table: Customer_Interactions
|   Customer_Interaction_ID |   Channel_ID |   Customer_ID |   Service_ID | Status_Code   | Services_and_Channels_Details   |
|--------------------------:|-------------:|--------------:|-------------:|:--------------|:--------------------------------|
|                        13 |           16 |            98 |          828 | Close         | bad                             |
|                        21 |           78 |            12 |          606 | Close         | good                            |
|                        71 |           16 |           295 |          430 | Close         | bad                             |
|                       117 |           45 |           103 |          313 | Open          | bad                             |
|                       169 |           65 |           119 |          828 | Open          | good                            |
|                       225 |           55 |           173 |          313 | Close         | bad                             |
|                       237 |           55 |           103 |          313 | Close         | bad                             |
|                       322 |           65 |            78 |          575 | Stuck         | bad                             |
|                       336 |           78 |           286 |          457 | Stuck         | good                            |
|                       514 |           55 |           113 |          313 | Close         | good                            |


Table: Integration_Platform
|   Integration_Platform_ID |   Customer_Interaction_ID | Integration_Platform_Details   |
|--------------------------:|--------------------------:|:-------------------------------|
|                       299 |                       225 | Success                        |
|                       447 |                       117 | Success                        |
|                       519 |                       607 | Success                        |
|                       536 |                       322 | Success                        |
|                       599 |                       322 | Success                        |
|                       605 |                       322 | Success                        |
|                       626 |                       117 | Success                        |
|                       677 |                       117 | Success                        |
|                       678 |                       552 | Fail                           |
|                       747 |                       322 | Success                        |


Table: Analytical_Layer
|   Analytical_ID |   Customers_and_Services_ID | Pattern_Recognition   | Analytical_Layer_Type_Code   |
|----------------:|----------------------------:|:----------------------|:-----------------------------|
|              11 |                         123 | Normal                | Bottom                       |
|              12 |                         203 | Normal                | Bottom                       |
|              17 |                         677 | Normal                | Middle                       |
|              32 |                         677 | Normal                | Middle                       |
|              36 |                         123 | Normal                | Middle                       |
|              39 |                         766 | Normal                | Top                          |
|              41 |                         606 | Normal                | Top                          |
|              48 |                         475 | Normal                | Top                          |
|              58 |                         123 | Special               | Top                          |
|              60 |                         228 | Normal                | Top                          |


Question: Which customer has used the most types of services ? Give me the customer details .

db_id: government_shift
tables: name: Services
data: Services.csv
name: Customers
data: Customers.csv
name: Channels
data: Channels.csv
name: Customers_and_Services
data: Customers_and_Services.csv
name: Customer_Interactions
data: Customer_Interactions.csv
name: Integration_Platform
data: Integration_Platform.csv
name: Analytical_Layer
data: Analytical_Layer.csv
label: select t1.customer_details from customers as t1 join customers_and_services as t2 on t1.customer_id  =  t2.customer_id group by t1.customer_details order by count(*) desc limit 1
sqlite_path: $MMTU_HOME/data_sqlite/spider/spider_data/test_database/government_shift/government_shift.sqlite