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    //! Converting decimal strings into IEEE 754 binary floating point numbers.
    //!
    //! # Problem statement
    //!
    //! We are given a decimal string such as `12.34e56`. This string consists of integral (`12`),
    //! fractional (`45`), and exponent (`56`) parts. All parts are optional and interpreted as zero
    //! when missing.
    //!
    //! We seek the IEEE 754 floating point number that is closest to the exact value of the decimal
    //! string. It is well-known that many decimal strings do not have terminating representations in
    //! base two, so we round to 0.5 units in the last place (in other words, as well as possible).
    //! Ties, decimal values exactly half-way between two consecutive floats, are resolved with the
    //! half-to-even strategy, also known as banker's rounding.
    //!
    //! Needless to say, this is quite hard, both in terms of implementation complexity and in terms
    //! of CPU cycles taken.
    //!
    //! # Implementation
    //!
    //! First, we ignore signs. Or rather, we remove it at the very beginning of the conversion
    //! process and re-apply it at the very end. This is correct in all edge cases since IEEE
    //! floats are symmetric around zero, negating one simply flips the first bit.
    //!
    //! Then we remove the decimal point by adjusting the exponent: Conceptually, `12.34e56` turns
    //! into `1234e54`, which we describe with a positive integer `f = 1234` and an integer `e = 54`.
    //! The `(f, e)` representation is used by almost all code past the parsing stage.
    //!
    //! We then try a long chain of progressively more general and expensive special cases using
    //! machine-sized integers and small, fixed-sized floating point numbers (first `f32`/`f64`, then
    //! a type with 64 bit significand, `Fp`). When all these fail, we bite the bullet and resort to a
    //! simple but very slow algorithm that involved computing `f * 10^e` fully and doing an iterative
    //! search for the best approximation.
    //!
    //! Primarily, this module and its children implement the algorithms described in:
    //! "How to Read Floating Point Numbers Accurately" by William D. Clinger,
    //! available online: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.4152
    //!
    //! In addition, there are numerous helper functions that are used in the paper but not available
    //! in Rust (or at least in core). Our version is additionally complicated by the need to handle
    //! overflow and underflow and the desire to handle subnormal numbers.  Bellerophon and
    //! Algorithm R have trouble with overflow, subnormals, and underflow. We conservatively switch to
    //! Algorithm M (with the modifications described in section 8 of the paper) well before the
    //! inputs get into the critical region.
    //!
    //! Another aspect that needs attention is the ``RawFloat`` trait by which almost all functions
    //! are parametrized. One might think that it's enough to parse to `f64` and cast the result to
    //! `f32`. Unfortunately this is not the world we live in, and this has nothing to do with using
    //! base two or half-to-even rounding.
    //!
    //! Consider for example two types `d2` and `d4` representing a decimal type with two decimal
    //! digits and four decimal digits each and take "0.01499" as input. Let's use half-up rounding.
    //! Going directly to two decimal digits gives `0.01`, but if we round to four digits first,
    //! we get `0.0150`, which is then rounded up to `0.02`. The same principle applies to other
    //! operations as well, if you want 0.5 ULP accuracy you need to do *everything* in full precision
    //! and round *exactly once, at the end*, by considering all truncated bits at once.
    //!
    //! FIXME Although some code duplication is necessary, perhaps parts of the code could be shuffled
    //! around such that less code is duplicated. Large parts of the algorithms are independent of the
    //! float type to output, or only needs access to a few constants, which could be passed in as
    //! parameters.
    //!
    //! # Other
    //!
    //! The conversion should *never* panic. There are assertions and explicit panics in the code,
    //! but they should never be triggered and only serve as internal sanity checks. Any panics should
    //! be considered a bug.
    //!
    //! There are unit tests but they are woefully inadequate at ensuring correctness, they only cover
    //! a small percentage of possible errors. Far more extensive tests are located in the directory
    //! `src/etc/test-float-parse` as a Python script.
    //!
    //! A note on integer overflow: Many parts of this file perform arithmetic with the decimal
    //! exponent `e`. Primarily, we shift the decimal point around: Before the first decimal digit,
    //! after the last decimal digit, and so on. This could overflow if done carelessly. We rely on
    //! the parsing submodule to only hand out sufficiently small exponents, where "sufficient" means
    //! "such that the exponent +/- the number of decimal digits fits into a 64 bit integer".
    //! Larger exponents are accepted, but we don't do arithmetic with them, they are immediately
    //! turned into {positive,negative} {zero,infinity}.

    #![doc(hidden)]
    #![unstable(feature = "dec2flt",
                reason = "internal routines only exposed for testing",
                issue = "0")]

    use fmt;
    use str::FromStr;

    use self::parse::{parse_decimal, Decimal, Sign, ParseResult};
    use self::num::digits_to_big;
    use self::rawfp::RawFloat;

    mod algorithm;
    mod table;
    mod num;
    // These two have their own tests.
    pub mod rawfp;
    pub mod parse;

    macro_rules! from_str_float_impl {
        ($t:ty) => {
            #[stable(feature = "rust1", since = "1.0.0")]
            impl FromStr for $t {
                type Err = ParseFloatError;

                /// Converts a string in base 10 to a float.
                /// Accepts an optional decimal exponent.
                ///
                /// This function accepts strings such as
                ///
                /// * '3.14'
                /// * '-3.14'
                /// * '2.5E10', or equivalently, '2.5e10'
                /// * '2.5E-10'
                /// * '5.'
                /// * '.5', or, equivalently,  '0.5'
                /// * 'inf', '-inf', 'NaN'
                ///
                /// Leading and trailing whitespace represent an error.
                ///
                /// # Arguments
                ///
                /// * src - A string
                ///
                /// # Return value
                ///
                /// `Err(ParseFloatError)` if the string did not represent a valid
                /// number.  Otherwise, `Ok(n)` where `n` is the floating-point
                /// number represented by `src`.
                #[inline]
                fn from_str(src: &str) -> Result<Self, ParseFloatError> {
                    dec2flt(src)
                }
            }
        }
    }
    from_str_float_impl!(f32);
    from_str_float_impl!(f64);

    /// An error which can be returned when parsing a float.
    ///
    /// This error is used as the error type for the [`FromStr`] implementation
    /// for [`f32`] and [`f64`].
    ///
    /// [`FromStr`]: ../str/trait.FromStr.html
    /// [`f32`]: ../../std/primitive.f32.html
    /// [`f64`]: ../../std/primitive.f64.html
    #[derive(Debug, Clone, PartialEq, Eq)]
    #[stable(feature = "rust1", since = "1.0.0")]
    pub struct ParseFloatError {
        kind: FloatErrorKind
    }

    #[derive(Debug, Clone, PartialEq, Eq)]
    enum FloatErrorKind {
        Empty,
        Invalid,
    }

    impl ParseFloatError {
        #[unstable(feature = "int_error_internals",
                   reason = "available through Error trait and this method should \
                             not be exposed publicly",
                   issue = "0")]
        #[doc(hidden)]
        pub fn __description(&self) -> &str {
            match self.kind {
                FloatErrorKind::Empty => "cannot parse float from empty string",
                FloatErrorKind::Invalid => "invalid float literal",
            }
        }
    }

    #[stable(feature = "rust1", since = "1.0.0")]
    impl fmt::Display for ParseFloatError {
        fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
            self.__description().fmt(f)
        }
    }

    fn pfe_empty() -> ParseFloatError {
        ParseFloatError { kind: FloatErrorKind::Empty }
    }

    fn pfe_invalid() -> ParseFloatError {
        ParseFloatError { kind: FloatErrorKind::Invalid }
    }

    /// Split decimal string into sign and the rest, without inspecting or validating the rest.
    fn extract_sign(s: &str) -> (Sign, &str) {
        match s.as_bytes()[0] {
            b'+' => (Sign::Positive, &s[1..]),
            b'-' => (Sign::Negative, &s[1..]),
            // If the string is invalid, we never use the sign, so we don't need to validate here.
            _ => (Sign::Positive, s),
        }
    }

    /// Convert a decimal string into a floating point number.
    fn dec2flt<T: RawFloat>(s: &str) -> Result<T, ParseFloatError> {
        if s.is_empty() {
            return Err(pfe_empty())
        }
        let (sign, s) = extract_sign(s);
        let flt = match parse_decimal(s) {
            ParseResult::Valid(decimal) => convert(decimal)?,
            ParseResult::ShortcutToInf => T::INFINITY,
            ParseResult::ShortcutToZero => T::ZERO,
            ParseResult::Invalid => match s {
                "inf" => T::INFINITY,
                "NaN" => T::NAN,
                _ => { return Err(pfe_invalid()); }
            }
        };

        match sign {
            Sign::Positive => Ok(flt),
            Sign::Negative => Ok(-flt),
        }
    }

    /// The main workhorse for the decimal-to-float conversion: Orchestrate all the preprocessing
    /// and figure out which algorithm should do the actual conversion.
    fn convert<T: RawFloat>(mut decimal: Decimal) -> Result<T, ParseFloatError> {
        simplify(&mut decimal);
        if let Some(x) = trivial_cases(&decimal) {
            return Ok(x);
        }
        // Remove/shift out the decimal point.
        let e = decimal.exp - decimal.fractional.len() as i64;
        if let Some(x) = algorithm::fast_path(decimal.integral, decimal.fractional, e) {
            return Ok(x);
        }
        // Big32x40 is limited to 1280 bits, which translates to about 385 decimal digits.
        // If we exceed this, we'll crash, so we error out before getting too close (within 10^10).
        let upper_bound = bound_intermediate_digits(&decimal, e);
        if upper_bound > 375 {
            return Err(pfe_invalid());
        }
        let f = digits_to_big(decimal.integral, decimal.fractional);

        // Now the exponent certainly fits in 16 bit, which is used throughout the main algorithms.
        let e = e as i16;
        // FIXME These bounds are rather conservative. A more careful analysis of the failure modes
        // of Bellerophon could allow using it in more cases for a massive speed up.
        let exponent_in_range = table::MIN_E <= e && e <= table::MAX_E;
        let value_in_range = upper_bound <= T::MAX_NORMAL_DIGITS as u64;
        if exponent_in_range && value_in_range {
            Ok(algorithm::bellerophon(&f, e))
        } else {
            Ok(algorithm::algorithm_m(&f, e))
        }
    }

    // As written, this optimizes badly (see #27130, though it refers to an old version of the code).
    // `inline(always)` is a workaround for that. There are only two call sites overall and it doesn't
    // make code size worse.

    /// Strip zeros where possible, even when this requires changing the exponent
    #[inline(always)]
    fn simplify(decimal: &mut Decimal) {
        let is_zero = &|&&d: &&u8| -> bool { d == b'0' };
        // Trimming these zeros does not change anything but may enable the fast path (< 15 digits).
        let leading_zeros = decimal.integral.iter().take_while(is_zero).count();
        decimal.integral = &decimal.integral[leading_zeros..];
        let trailing_zeros = decimal.fractional.iter().rev().take_while(is_zero).count();
        let end = decimal.fractional.len() - trailing_zeros;
        decimal.fractional = &decimal.fractional[..end];
        // Simplify numbers of the form 0.0...x and x...0.0, adjusting the exponent accordingly.
        // This may not always be a win (possibly pushes some numbers out of the fast path), but it
        // simplifies other parts significantly (notably, approximating the magnitude of the value).
        if decimal.integral.is_empty() {
            let leading_zeros = decimal.fractional.iter().take_while(is_zero).count();
            decimal.fractional = &decimal.fractional[leading_zeros..];
            decimal.exp -= leading_zeros as i64;
        } else if decimal.fractional.is_empty() {
            let trailing_zeros = decimal.integral.iter().rev().take_while(is_zero).count();
            let end = decimal.integral.len() - trailing_zeros;
            decimal.integral = &decimal.integral[..end];
            decimal.exp += trailing_zeros as i64;
        }
    }

    /// Quick and dirty upper bound on the size (log10) of the largest value that Algorithm R and
    /// Algorithm M will compute while working on the given decimal.
    fn bound_intermediate_digits(decimal: &Decimal, e: i64) -> u64 {
        // We don't need to worry too much about overflow here thanks to trivial_cases() and the
        // parser, which filter out the most extreme inputs for us.
        let f_len: u64 = decimal.integral.len() as u64 + decimal.fractional.len() as u64;
        if e >= 0 {
            // In the case e >= 0, both algorithms compute about `f * 10^e`. Algorithm R proceeds to
            // do some complicated calculations with this but we can ignore that for the upper bound
            // because it also reduces the fraction beforehand, so we have plenty of buffer there.
            f_len + (e as u64)
        } else {
            // If e < 0, Algorithm R does roughly the same thing, but Algorithm M differs:
            // It tries to find a positive number k such that `f << k / 10^e` is an in-range
            // significand. This will result in about `2^53 * f * 10^e` < `10^17 * f * 10^e`.
            // One input that triggers this is 0.33...33 (375 x 3).
            f_len + (e.abs() as u64) + 17
        }
    }

    /// Detect obvious overflows and underflows without even looking at the decimal digits.
    fn trivial_cases<T: RawFloat>(decimal: &Decimal) -> Option<T> {
        // There were zeros but they were stripped by simplify()
        if decimal.integral.is_empty() && decimal.fractional.is_empty() {
            return Some(T::ZERO);
        }
        // This is a crude approximation of ceil(log10(the real value)). We don't need to worry too
        // much about overflow here because the input length is tiny (at least compared to 2^64) and
        // the parser already handles exponents whose absolute value is greater than 10^18
        // (which is still 10^19 short of 2^64).
        let max_place = decimal.exp + decimal.integral.len() as i64;
        if max_place > T::INF_CUTOFF {
            return Some(T::INFINITY);
        } else if max_place < T::ZERO_CUTOFF {
            return Some(T::ZERO);
        }
        None
    }

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