Darja Donga

Darja Donga (transl. Royal Thief) is a 1985 Telugu-language action thriller film, produced by R. Ramakrishnam Raju under the Sri Vijayalakshmi Arts banner[2] and directed by Manivannan.[3] It stars Suman and Vijayashanti in the lead roles, Sathyaraj as the main antagonist, Rajendra Prasad in a supporting role, with music composed by Ilaiyaraaja.[4] The film was dubbed into Tamil as Marma Manithan.[5]

Darja Donga
Theatrical release poster
Directed byManivannan
Produced byR. Ramakrishnam Raju
Written bySainath Thotapalli (dialogues)
Screenplay byManivannan
Story bySathyaraj
StarringSuman
Vijayashanti
Sathyaraj
Rajendra Prasad
Music byIlaiyaraaja
CinematographySabhapathi
Edited byKotagiri Venkateswara Rao
Production
company
Sri Vijayalakshmi Arts[1]
Release date
  • 14 June 1985 (1985-06-14)
CountryIndia
LanguageTelugu

Cast

Soundtrack

Darja Donga
Film score by
Released1985
GenreSoundtrack
Length22:07
LabelECHO Audio
ProducerIlaiyaraaja
Ilaiyaraaja chronology
Preminchu Pelladu
(1985)
Darja Donga
(1985)
Sindhu Bhairavi
(1985)

Music was composed by Ilaiyaraaja. Lyrics were written by Veturi Sundararama Murthy. Music released on ECHO Audio Company.

S. No.Song TitleSingerslength
1 "Chali Chali" SP Balu, S. Janaki 4:17
2 "Hello Hello Chalaaki Pilla" SP Balu 4:37
3 "Manasula Gusa Gusa" SP Balu, S. Janaki 4:29
4 "Naalo Chinukulatho" SP Balu, S. Janaki 4:22
5 "Thappu Kaadhuraa" SP Balu, S. Janaki 4:22
gollark: I didn't do any horrible homoglyph hacks with THAT.
gollark: It uses the function, yes.
gollark: So, I finished that to highly dubious demand. I'd like to know how #11 and such work.
gollark: > `x = _(int(0, e), int(e, е))`You may note that this would produce slices of 0 size. However, one of the `e`s is a homoglyph; it contains `2 * e`.`return Result[0][0], x, m@set({int(e, 0), int(е, e)}), w`From this, it's fairly obvious what `strassen` *really* does - partition `m1` into 4 block matrices of half (rounded up to the nearest power of 2) size.> `E = typing(lookup[2])`I forgot what this is meant to contain. It probably isn't important.> `def exponentiate(m1, m2):`This is the actual multiplication bit.> `if m1.n == 1: return Mаtrix([[m1.bigData[0] * m2.bigData[0]]])`Recursion base case. 1-sized matrices are merely multiplied scalarly.> `aa, ab, ac, ad = strassen(m1)`> `аa, аb, аc, аd = strassen(m2)`More use of homoglyph confusion here. The matrices are quartered.> `m = m1.subtract(exponentiate(aa, аa) ** exponentiate(ab, аc), exponentiate(aa, аb) ** exponentiate(ab, аd), exponentiate(ac, аa) ** exponentiate(ad, аc), exponentiate(ac, аb) ** exponentiate(ad, аd)) @ [-0j, int.abs(m2.n * 3, m1.n)]`This does matrix multiplication in an inefficient *recursive* way; the Strassen algorithm could save one of eight multiplications here, which is more efficient (on big matrices). It also removes the zero padding.> `m = exponentiate(Mаtrix(m1), Mаtrix(m2)) @ (0j * math.sin(math.asin(math.sin(math.asin(math.sin(math.e))))), int(len(m1), len(m1)))`This multiples them and I think also removes the zero padding again, as we want it to be really very removed.> `i += 1`This was added as a counter used to ensure that it was usably performant during development.> `math.factorial = math.sinh`Unfortunately, Python's factorial function has really rather restrictive size limits.> `for row in range(m.n):`This converts back into the 2D array format.> `for performance in sorted(dir(gc)): getattr(gc, performance)()`Do random fun things to the GC.
gollark: > `globals()[Row + Row] = random.randint(*sys.version_info[:2])`Never actually got used anywhere.> `ε = sys.float_info.epsilon`Also not used. I just like epsilons.> `def __exit__(self, _, _________, _______):`This is also empty, because cleaning up the `_` global would be silly. It'll be overwritten anyway. This does serve a purpose, however, and not just in making it usable as a context manager. This actually swallows all errors, which is used in some places.> `def __pow__(self, m2):`As ever, this is not actual exponentiation. `for i, (ι, 𐌉) in enumerate(zip(self.bigData, m2.bigData)): e.bigData[i] = ι + 𐌉` is in fact just plain and simple addition of two matrices.> `def subtract(forth, 𝕒, polynomial, c, vector_space):`This just merges 4 submatrices back into one matrix.> `with out as out, out, forth:`Apart from capturing the exceptions, this doesn't really do much either. The `_` provided by the context manager is not used.> `_(0j, int(0, 𝕒.n))`Yes, it's used in this line. However, this doesn't actually have any effect whatsoever on the execution of this. So I ignore it. It was merely a distraction.> `with Mаtrix(ℤ(ℤ(4))):`It is used again to swallow exceptions. After this is just some fluff again.> `def strassen(m, x= 3.1415935258989):`This is an interesting part. Despite being called `strassen`, it does not actually implement the Strassen algorithm, which is a somewhat more efficient way to multiply matrices than the naive way used in - as far as I can tell - every entry.> `e = 2 ** (math.ceil(math.log2(m.n)) - 1)`This gets the next power of two in a fairly obvious way. It is used to pad out the matrix to the next power of 2 size.> `with m:`The context manager is used again for nicer lookups.> `Result[0] += [_(0j, int(e, e))]`Weird pythonoquirkiness again. You can append to lists in tuples with `+=`, but it throws an exception as they're sort of immutable.> `typing(lookup[4])(input())`It's entirely possible that this does things.

References


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