You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 movie_3
【Schema】
# Table: country
[
(country_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(country:TEXT, Examples: [Afghanistan, Algeria, American Samoa]),
(last_update:DATETIME, Examples: [2006-02-15 04:44:00])
]
# Table: customer
[
(customer_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(store_id:INTEGER, Examples: [1, 2]),
(first_name:TEXT, Examples: [MARY, PATRICIA, LINDA]),
(last_name:TEXT, Examples: [SMITH, JOHNSON, WILLIAMS]),
(email:TEXT),
(address_id:INTEGER, Examples: [5, 6, 7]),
(active:INTEGER, Examples: [1, 0]),
(create_date:DATETIME, Examples: [2006-02-14 22:04:36]),
(last_update:DATETIME, Examples: [2006-02-15 04:57:20])
]
# Table: film_text
[
(film_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(title:TEXT, Examples: [ACADEMY DINOSAUR, ACE GOLDFINGER, ADAPTATION HOLES]),
(description:TEXT)
]
# Table: store
[
(store_id:INTEGER, Primary Key, Examples: [1, 2]),
(manager_staff_id:INTEGER, Examples: [1, 2]),
(address_id:INTEGER, Examples: [1, 2]),
(last_update:DATETIME, Examples: [2006-02-15 04:57:12])
]
# Table: film_category
[
(film_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(category_id:INTEGER, Primary Key, Examples: [6, 11, 8]),
(last_update:DATETIME, Examples: [2006-02-15 05:07:09])
]
# Table: payment
[
(payment_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(customer_id:INTEGER, Examples: [1, 2, 3]),
(staff_id:INTEGER, Examples: [1, 2]),
(rental_id:INTEGER, Examples: [76, 573, 1185]),
(amount:REAL, Examples: [2.99, 0.99, 5.99]),
(payment_date:DATETIME, Examples: [2005-05-25 11:30:37]),
(last_update:DATETIME, Examples: [2006-02-15 22:12:30])
]
# Table: actor
[
(actor_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(first_name:TEXT, Examples: [PENELOPE, NICK, ED]),
(last_name:TEXT, Examples: [GUINESS, WAHLBERG, CHASE]),
(last_update:DATETIME, Examples: [2006-02-15 04:34:33])
]
# Table: rental
[
(rental_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(rental_date:DATETIME, Examples: [2005-05-24 22:53:30]),
(inventory_id:INTEGER, Examples: [367, 1525, 1711]),
(customer_id:INTEGER, Examples: [130, 459, 408]),
(return_date:DATETIME, Examples: [2005-05-26 22:04:30]),
(staff_id:INTEGER, Examples: [1, 2]),
(last_update:DATETIME, Examples: [2006-02-15 21:30:53])
]
# Table: language
[
(language_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(name:TEXT, Examples: [English, Italian, Japanese]),
(last_update:DATETIME, Examples: [2006-02-15 05:02:19])
]
# Table: film
[
(film_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(title:TEXT, Examples: [ACADEMY DINOSAUR, ACE GOLDFINGER, ADAPTATION HOLES]),
(description:TEXT),
(release_year:TEXT, Examples: [2006]),
(language_id:INTEGER, Examples: [1]),
(original_language_id:INTEGER),
(rental_duration:INTEGER, Examples: [6, 3, 7]),
(rental_rate:REAL, Examples: [0.99, 4.99, 2.99]),
(length:INTEGER, Examples: [86, 48, 50]),
(replacement_cost:REAL, Examples: [20.99, 12.99, 18.99]),
(rating:TEXT, Examples: [PG, G, NC-17]),
(special_features:TEXT, Examples: [Deleted Scenes,Behind the Scenes]),
(last_update:DATETIME, Examples: [2006-02-15 05:03:42])
]
# Table: inventory
[
(inventory_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(film_id:INTEGER, Examples: [1, 2, 3]),
(store_id:INTEGER, Examples: [1, 2]),
(last_update:DATETIME, Examples: [2006-02-15 05:09:17])
]
# Table: address
[
(address_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(address:TEXT, Examples: [47 MySakila Drive, 28 MySQL Boulevard, 23 Workhaven Lane]),
(address2:TEXT),
(district:TEXT, Examples: [Alberta, QLD, Nagasaki]),
(city_id:INTEGER, Examples: [300, 576, 463]),
(postal_code:TEXT, Examples: [35200, 17886, 83579]),
(phone:TEXT, Examples: [14033335568, 6172235589, 28303384290]),
(last_update:DATETIME, Examples: [2006-02-15 04:45:30])
]
# Table: film_actor
[
(actor_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(film_id:INTEGER, Primary Key, Examples: [1, 23, 25]),
(last_update:DATETIME, Examples: [2006-02-15 05:05:03])
]
# Table: category
[
(category_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(name:TEXT, Examples: [Action, Animation, Children]),
(last_update:DATETIME, Examples: [2006-02-15 04:46:27])
]
# Table: city
[
(city_id:INTEGER, Primary Key, Examples: [1, 2, 3]),
(city:TEXT, Examples: [A Corua (La Corua), Abha, Abu Dhabi]),
(country_id:INTEGER, Examples: [87, 82, 101]),
(last_update:DATETIME, Examples: [2006-02-15 04:45:25])
]
# Table: staff
[
(staff_id:INTEGER, Primary Key, Examples: [1, 2]),
(first_name:TEXT, Examples: [Mike, Jon]),
(last_name:TEXT, Examples: [Hillyer, Stephens]),
(address_id:INTEGER, Examples: [3, 4]),
(picture:BLOB),
(email:TEXT),
(store_id:INTEGER, Examples: [1, 2]),
(active:INTEGER, Examples: [1]),
(username:TEXT, Examples: [Mike, Jon]),
(password:TEXT, Examples: [8cb2237d0679ca88db6464eac60da96345513964]),
(last_update:DATETIME, Examples: [2006-02-15 04:57:16])
]
【Foreign keys】
customer.address_id=address.address_id
customer.store_id=store.store_id
store.address_id=address.address_id
store.manager_staff_id=staff.staff_id
film_category.category_id=category.category_id
film_category.film_id=film.film_id
payment.rental_id=rental.rental_id
payment.staff_id=staff.staff_id
payment.customer_id=customer.customer_id
rental.staff_id=staff.staff_id
rental.customer_id=customer.customer_id
rental.inventory_id=inventory.inventory_id
film.original_language_id=language.language_id
film.language_id=language.language_id
inventory.store_id=store.store_id
inventory.film_id=film.film_id
address.city_id=city.city_id
film_actor.film_id=film.film_id
film_actor.actor_id=actor.actor_id
city.country_id=country.country_id
staff.store_id=store.store_id
staff.address_id=address.address_id

    【Question】
    Among the films that are released in 2006, how many of them are rated Adults Only in the Motion Picture Association Film Rating? released in 2006 refers to release_year = 2006; rated Adults Only refers to rating = 'NC-17'

    【Evidence】
    released in 2006 refers to release_year = 2006; rated Adults Only refers to rating = 'NC-17'

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(film_id) FROM film WHERE rating = 'NC-17' AND release_year = 2006