kandi X-RAY | DeepCopy Summary
kandi X-RAY | DeepCopy Summary
DeepCopy helps you create deep copies (clones) of your objects. It is designed to handle cycles in the association graph.
Top functions reviewed by kandi - BETA
- Clone an object .
- Copy an object property
- Get class properties
- Get a property .
- Create a deep copy closure for the list .
- Set the bprop property
- Set element s property
- Set the property2
- Set property1 .
- Get property .
DeepCopy Key Features
DeepCopy Examples and Code Snippets
def __deepcopy__(self, memo): """Perform a deepcopy of the `DistributedVariable`. Unlike the deepcopy of a regular tf.Variable, this keeps the original strategy and devices of the `DistributedVariable`. To avoid confusion with the b
def __deepcopy__(self, memo): """Perform a deepcopy of the `AggregatingVariable`. Unlike the deepcopy of a regular tf.Variable, this keeps the original strategy and devices of the `AggregatingVariable`. To avoid confusion with the b
def __deepcopy__(self, memo): # We check the check health thread instead of whether we are in eager mode # to limit the backward incompatibility. if hasattr(self, "_check_health_thread"): raise ValueError( "MultiWorkerMirr
Trending Discussions on DeepCopy
An example illustrates it best
Starting with a list of N unique items = ['A','B','C','D','E']
Pick k=2 items
Here we have Python implementation to show the number of possible combinations:...
ANSWERAnswered 2022-Mar-06 at 05:22
Given a way to create all partitions of a set into equal-size bins with no 'left-over'/smaller bins, you can easily write a function to get all partitions with a left-over, by iterating first over all combinations of the 'left-over' size and appending those to each partition of the other elements.
Using the set partitions function from Gareth Rees' answer here, you can do this:
I'd like to type-annotate abstract class method witch behave as a constructor. For example in the code below,
ElementBase.from_data is meant to be a abstract classmethod constructor.
ANSWERAnswered 2022-Feb-22 at 02:30
It looks like
mypy doesn't understand the
abstractclassmethod decorator. That decorator has been deprecated since Python 3.3, as the
classmethod decorators were updated to play nice together. I think your code will work properly if you do:
Suppose we apply a set of (inplace) operations within a for-loop on mostly the same fudamental data (mutable). What is a memory efficient (and thread safe) way to do so?
Note the fundamental data should not be altered within the for-loop from iteration to iteration.
Assume we have some Excel files containing fundamental data in a
data directory. Further we have some addtional data in the
some_more_data directory. I want to apply operations on the data retrieved from the
data directory using the files from the
some_more_data directory. Afterwards I want to print the results to a new pickle file.
ANSWERAnswered 2022-Jan-03 at 13:36
raw_data dictionary has been created, I don't see where it is ever modified (after all, that is the point of using
deepcopy on it). So while deep-copying a mutable object is not thread safe, this particular object is not undergoing mutation at any time. So I don't see why there would be an issue. But, you could always do the
deepcopy under control of a lock if you were not confident.
If you are doing this with multithreading, then using a
threading.Lock is probably not going to cost you in performance since the deepcopy operation is all CPU and you cannot achieve any
deepcopy parallelism anyway because your thread is already locking on the Global Interpreter Lock (GIL) for that function (it is primarily Python bytecode). This additional locking just prevents giving up your time slice while in the middle of a
deepcopy operation to another thread that might begin a
deepcopy operation (but again, I still don't think that is an issue). But if you are using multithreading, then what performance increase will you be getting from doing concurrent I/O operations? Depending on whether you have a hard disk drive or solid state drive and what the characteristics of that drive is, concurrency might even hurt your I/O performance. You may get some performance improvement from the
Pandas operations if they release the GIL.
Multiprocessing, which does provide true parallelism of CPU-intensive functions, has its own overhead in the creation of the processes and in passing data from one address space to another (i.e. one process to another). This additional overhead that you do not have in serial processing has to be compensated for by the savings achieved by parallelizing your calculations. It's not clear from what you have shown, if that is indeed representative of your actual situation, that you would gain anything from that parallelism. But, then, of course, you would not have to worry about the thread safety of
deepcopy since once each process has a copy of
raw_data that process would be running a single thread with its own copy of memory totally isolated from one another.
deepcopyis not thread safe for mutable objects but since your object does not appear to be "mutating", it shouldn't be an issue. But if running under multithreading, you could do the
deepcopyoperation as an atomic operation under control of a
multithreading.Lockwithout any significant loss in performance.
If you are using multiprocessing, and assuming
raw_datawas not being implemented in shared memory, then each process would be working on its own copy of
raw_datato begin with. So even if another process were "mutating"
raw_data, as long as any one process was running a single thread, there is no need to worry about the thread safety of
It's not clear whether multithreading or multiprocessing will achieve any performance improvements based on the code I have seen.
This benchmarks serial, multithreading and multiprocessing. Perhaps with only 2 keys in each dictionary this is not a realistic example but it gives a general idea:
I followed a PyTorch tutorial to learn reinforcement learning(TRAIN A MARIO-PLAYING RL AGENT) but I am confused about the following code:...
ANSWERAnswered 2021-Dec-23 at 11:07
Essentially, what happens here is that the output of the net is being sliced to get the desired part of the Q table.
The (somewhat confusing) index of
[np.arange(0, self.batch_size), action] indexes each axis. So, for axis with index 1, we pick the item indicated by
action. For index 0, we pick all items between 0 and
self.batch_size is the same as the length of dimension 0 of this array, then this slice can be simplified to
[:, action] which is probably more familiar to most users.
I'm trying to check for a certain chain of events in an LTTNG event log using Babeltrace 1. The LTTNG log is loaded using a Babeltrace collection:...
ANSWERAnswered 2021-Dec-16 at 14:34
Babeltrace co-maintainer here.
Indeed, Babeltrace 1 reuses the same event record object for each iteration step. This means you cannot keep an "old" event record alive as its data changes behind the scenes.
The Python bindings of Babeltrace 1 are rudimental wrappers of the library objects. This means the same constraints apply. Also, Babeltrace 1 doesn't offer any event record object copying function, so anything like
copy.copy() will only copy internal pointers which will then exhibit the same issue.
Babeltrace (1 and 2) iterators cannot go backwards for performance reasons (more about this below).
The only solution I see is making your own event record copying function, keeping what's necessary in another instance of your own class. After all, you probably only need the name, timestamp, and some first-level fields of the event record.
But Babeltrace 2 is what you're looking for, especially since we don't maintain Babeltrace 1 anymore (except for critical/security bug fixes).
Babeltrace 2 offers a rich and consistent C API where many objects have a reference count and therefore can live as long as you like. The Babeltrace 2 Python bindings wrap this C API so that you can benefit from the same features.
About your comment:
since it seems the events are a kind of linked list where one could walk backward
No, you cannot. This is to accomodate limitations of some trace formats, in particular CTF (the format which LTTng uses). A CTF packet is a sequence of serialized binary event records: to decode event record N, you need to decode event record N - 1 first, and so on. A CTF packet can contain thousands of contiguous event records like this, CTF data streams can contain thousands of packets, and a CTF trace can contain many data streams. Knowing this, there would be no reasonable way to store the offsets of all the encoded CTF event records so that you can iterate backwards without heavy object copies.
What you can do however with Babeltrace 2 is keep the specific event record objects you need, without any copy.
In the future, we'd like a way to copy a message iterator, duplicating all its state and what's needed to continue behind the scenes. This would make it possible to keep "checkpoint iterators" so that you can go back to previous event records if you can't perform your analysis in one pass for some reason.
Note that you can also make a message iterator seek a specific timestamp, but "fast" seeking is not implemented as of this date in the
ctf plugin (the iterator seeks the beginning of the message sequence and then advances until it reaches the requested timestamp, which is not efficient).
I have one question. I was doing game in pygame "game of life". I have one aim. I don't know how to add endless field to my game. can you help me, please? adding, sorry - my english is very bad(. i want to add endless field to my game. Stack overflow says me to add some txt. i don't know why...
ANSWERAnswered 2021-Dec-12 at 15:36
If I understand you correctly, there is only one condition you need to change. Use the
% (modulo) operator to compute the remainder of an integer division:
if 0 < xx < self.width and 0 < yy < self.height and self.board[yy][xx]:
I seem to be able to add tokens without issue but if I try to add a suffix (ie.. one that doesn't have the init character
'Ġ' at the front), the tokenizer doesn't put spaces in the right spots. Here's some very simplified test code.
ANSWERAnswered 2021-Nov-29 at 23:52
The short answer is that there's "behavior" (bug?) in the handling of added tokens for Bart (and RoBerta, GPT2, etc..) that explicitly strips spaces from the tokens adjacent (both left and right) to the added token's location. I don't see a simple work-around to this.
Added tokens are handled differently in the transformer's tokenizer code. The text is first split, using a
Trie to identify any tokens in the added tokens list (see
tokenization_utils.py::tokenize()). After finding any added tokens in the text, the remainder is then tokenized using the existing vocab/bpe encoding scheme (see
The added tokens are added to the
self.unique_no_split_tokens list which prevents them from being broken down further, into smaller chunks. The code that handles this (see
tokenization_utils.py::tokenize() explicitly strips the spaces from the tokens to the left and right.
You could manually remove them from the "no split" list but then they may be broken down into smaller sub-components.
Note that for "special tokens", if you add the token inside of the
AddedToken class you can set the
rstrip behaviors but this isn't available for non-special tokens.
See https://github.com/huggingface/transformers/blob/v4.12.5-release/src/transformers/tokenization_utils.py#L517 for the else statement where the spaces are stripped.
My problem is quite simple but I am unable to solve it. When I insert objects into a list, the elements of the list all change whenever I change one of them (they all point to the same object in the memory I think). I want to unlink them so the list would not be full of the exactly same objects with the same values. E.g. avoid linking or mutability. I think the problem is how I initialize the objects but I am not sure how to solve it. Here is my code....
ANSWERAnswered 2021-Nov-16 at 10:25
There are some fundamental mistakes you have made in the code. Let me try to put some light on those first , using your lines of code
I have a google cloud function triggered by a topic pubsub.
Most of the times, everything works fine like this:...
ANSWERAnswered 2021-Nov-11 at 08:38
The missing logs entries from Cloud Functions is not something usually expected and could be caused by termination of the process before it reach the phase where the logs are forwarded to Cloud Monitoring. The missing log entry is not typically a concern but could indicate some unbalanced configuration. The premature resource termination can be caused by exhausting some limit. It looks like your function takes significant amount of time.
I want to define many methods in my class
I want to call them by their name
I do not want to call them indirectly for example through an intermediate method like
TestClass().use_method('method_1', params) to keep consistency with other parts of the code.
I want to define dynamically my numerous methods, but I do not understand why this minimal example does not work:...
ANSWERAnswered 2021-Oct-21 at 20:35
This is one of the classic Python stumbles. Your get the value of the variable, and the variable ends up with the final value.
You can do what you want by "capturing" the value of the variable as a default:
No vulnerabilities reported
PHP requires the Visual C runtime (CRT). The Microsoft Visual C++ Redistributable for Visual Studio 2019 is suitable for all these PHP versions, see visualstudio.microsoft.com. You MUST download the x86 CRT for PHP x86 builds and the x64 CRT for PHP x64 builds. The CRT installer supports the /quiet and /norestart command-line switches, so you can also script it.
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