Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug


Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug

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How fix Azure web app deploy error with VSCode?

Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug


Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug
Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug
Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug
Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug
Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug
Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug
Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug
Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug

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Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug


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ImportError: cannot import name ‘_unicodefun’ from ‘click’

if running our lint checks with the python black pkg. an error comes up

ImportError: cannot import name '_unicodefun' from 'click' (/Users/robot/.cache/pre-commit/repo3u71ccm2/py_env-python3.9/lib/python3.9/site-packages/click/__init__.py)

related issues:

https://github.com/psf/black/issues/2976
https://github.com/dask/distributed/issues/6013

 

Answer 1

This has been fixed by Black 22.3.0. Versions before that won’t work with click 8.1.0.

https://github.com/psf/black/issues/2964

E.g.: black.yml

 

          python-version: 3.8
      - name: install black
        run: |
-          pip install black==20.8b1
+          pip install black==22.3.0
      - name: run black
        run: |
          black . --check --line-length 100

https://github.com/Clinical-Genomics/cgbeacon2/pull/221/files

As a workaround pin click to last version via pip install --upgrade click==8.0.2

MUI installation doesn’t work with React 18

i was trying to install material ui core and icons with my react 18.0 project but i can’t.The project has been created using the latest create-react-app npm install @material-ui/core @material-ui/icons

npm ERR! code ERESOLVE
npm ERR! ERESOLVE unable to resolve dependency tree
npm ERR!
npm ERR! While resolving: client@0.1.0
npm ERR! Found: react@18.0.0
npm ERR! node_modules/react
npm ERR!   react@"^18.0.0" from the root project
npm ERR!
npm ERR! Could not resolve dependency:
npm ERR! peer react@"^16.8.0 || ^17.0.0" from @material-ui/core@4.12.3
npm ERR! node_modules/@material-ui/core
npm ERR!   peer @material-ui/core@"^4.0.0" from @material-ui/icons@4.11.2
npm ERR!   node_modules/@material-ui/icons
npm ERR!     @material-ui/icons@"*" from the root project
npm ERR!
npm ERR! Fix the upstream dependency conflict, or retry
npm ERR! this command with --force, or --legacy-peer-deps
npm ERR! to accept an incorrect (and potentially broken) dependency resolution.

Answer 1:

npm install @mui/material @emotion/react @emotion/styled --legacy-peer-deps
npm install @mui/icons-material --legacy-peer-deps

Github Issue: https://github.com/mui/material-ui/issues/32074

How can I convert std::vector to a vector of pairs std::vector<:pair>> using an STL algorithm?

I have a vector of integers:

std::vector values = {1,2,3,4,5,6,7,8,9,10};

Given that values.size() will always be even.

I simply want to convert the adjacent elements into a pair, like this:

std::vector<std::pair<int,int>> values = { {1,2}, {3,4} , {5,6}, {7,8} ,{9,10} };

I.e., the two adjacent elements are joined into a pair.

What STL algorithm can I use to easily achieve this? Is it possible to achieve this through some standard algorithms?

Of course, I can easily write an old-school indexed for loop to achieve that. But I want to know what the simplest solution could look like using range-based for loops or any other STL algorithm, like std::transform, etc.

Answer 1
Once we have C++23’s extension to , you can get most of the way there with std::ranges::views::chunk, although that produces subranges, not pairs.

#include
#include
#include </code>

int main()
{
std::vector values = {1,2,3,4,5,6,7,8,9,10};
auto chunk_to_pair = [](auto chunk)
{
return std::pair(*chunk.begin(), *std::next(chunk.begin()));
};
for (auto [first, second] : values | std::ranges::views::chunk(2) | std::ranges::views::transform(chunk_to_pair))
{
std::cout &lt;&lt; first &lt;&lt; second &lt;&lt; std::endl;
}
}

Alternatively, you could achieve a similar result by ziping a pair of strided views

#include
#include
#include

int main()
{
std::vector values = {1,2,3,4,5,6,7,8,9,10};
auto odds = values | std::ranges::views::drop(0) | std::ranges::views::stride(2);
auto evens = values | std::ranges::views::drop(1) | std::ranges::views::stride(2);
for (auto [first, second] : std::ranges::views::zip(odds, evens))
{
std::cout &lt;&lt; first &lt;&lt; second &lt;&lt; std::endl;
}
}

Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug


Null pointer check via “myPtr > 0”

In some legacy code I came across the following null pointer check.

if( myPtr > 0 ) {

}

Answer 1:

Are there any technical risks of checking for a null pointer via this if-check?

Ordered comparison between a pointer and an integer is ill-formed in C++ (even when the integer is a null pointer constant such as it is in this case). The risk is that compilers are allowed to, and do, refuse to compile such code.

You can rewrite it as either of these:

if(myPtr != nullptr)
if(myPtr)

Running a fine-tune model for my CNN : Value Error

So I am trying to use a pre-trained model on my data set to then compare it to my own cnn model. However, I see an error as soon as I try to do model. fit so much that ((None, 4, 4, 1) vs (None,)). Where is this error coming from? Am I supposed to edit the pre-tune cnn.

The model that I am using is ResNET50 with no modification except the input layer changed to 128 and there are 2 outputs.

Any help is welcome,

CODE:
history = modelB.fit_generator(train_data,
validation_data = test_data,
epochs=5,
steps_per_epoch = 1714,)

ERROR

---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
in ()
2 validation_data = test_data,
3 epochs=5,
----> 4 steps_per_epoch = 1714,)

2 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
1145 except Exception as e: # pylint:disable=broad-except
1146 if hasattr(e, "ag_error_metadata"):
-> 1147 raise e.ag_error_metadata.to_exception(e)
1148 else:
1149 raise

ValueError: in user code:

File "http://invalidtype.com/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function *
return step_function(self, iterator)
File "http://invalidtype.com/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "http://invalidtype.com/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1000, in run_step **
outputs = model.train_step(data)
File "http://invalidtype.com/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "http://invalidtype.com/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 919, in compute_loss
y, y_pred, sample_weight, regularization_losses=self.losses)
File "http://invalidtype.com/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "http://invalidtype.com/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 141, in __call__
losses = call_fn(y_true, y_pred)
File "http://invalidtype.com/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 245, in call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "http://invalidtype.com/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 1932, in binary_crossentropy
backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits),
File "http://invalidtype.com/usr/local/lib/python3.7/dist-packages/keras/backend.py", line 5247, in binary_crossentropy
return tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)

ValueError: `logits` and `labels` must have the same shape, received ((None, 4, 4, 1) vs (None,)).

Answer 1:

The Issue is with the loss function used when you compile the model.

Replace the compile with below code:

model.compile(optimizer='adam',loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])

Answer 2:

Use tf.keras.utils.plot_model to print out a graphic representation of the model. you have a mismatch between the number of input and output nodes.

Bitbucket Cloud recently stopped supporting account passwords for Git authentication

The Problem

I have pushed the code in the morning it was working fine but now I’m trying to push the code but getting this error: Bitbucket Cloud recently stopped supporting account passwords for Git authentication

Answer 1:

I’ve also faced this issue.

  1. Then I opened my Bitbucket account. 2)Then at the bottom left corner you will find an icon with your username’s initials. 3)Click on that and go to settings and click on Personal Settings 4)In personal Settings, in the left panel you will find “access management”
  2. Under that, you will find “app passwords”
  3. Click on that –> Then you will be routed to create an app password page
  4. There you can give the required permissions and create a password (Note: You have to store that password somewhere as it will be displayed only once) 8)When you push the code to any repo…use your app password instead of your account password (No need to change any settings in the git_config file)

Happy programming :)!

Action requested: Declare your Ad ID permission

The Problem:

Today I have got this email:

Last July, we announced Advertising policy changes to help bolster security and privacy. We added new restrictions on identifiers used by apps that target children. When users choose to delete their advertising ID in order to opt out of personalization advertising, developers will receive a string of zeros instead of the identifier if they attempt to access the identifier. This behavior will extend to phones, tablets, and Android TV starting April 1, 2022. We also announced that you need to declare an AD_ID permission when you update your app targeting API level to 31 (Android 12). Today, we are sharing that we will give developers more time to ease the transition. We will require this permission declaration when your apps are able to target Android 13 instead of starting with Android 12.

Action Items If you use an advertising ID, you must declare the AD_ID Permission when your app targets Android 13 or above. Apps that don’t declare the permission will get a string of zeros. Note: You’ll be able to target Android 13 later this year. If your app uses an SDK that has declared the Ad ID permission, it will acquire the permission declaration through manifest merge. If your app’s target audience includes children, you must not transmit Android Advertising ID (AAID) from children or users of unknown age.

My app is not using the Advertising ID. Should I declare the AD_ID Permission in Manifest or not?

Answer 1:

If your app uses the Google Mobile Ads SDK(Admob) version 20.4.0 or higher, you can skip setting up the permission manually since the SDK automatically declares it

More information here:

https://developers.google.com/admob/android/quick-start

ImportError: cannot import name ‘safe_str_cmp’ from ‘werkzeug.security’

The problem

any ideas why this error?

my project was working fine, i copied it to an external drive and onto my laptop to work on the road, it worked fine. i copied back to my desktop and had a load of issues with invalid interpreters etc, so i made a new project and copied just the scripts in, made a new requirements.txt and installed all the packages, but when i run i get this error

Traceback (most recent call last):
File "E:\Dev\spot_new\flask_blog\run.py", line 1, in
from flaskblog import app
File "E:\Dev\spot_new\flask_blog\flaskblog\__init__.py", line 3, in
from flask_bcrypt import Bcrypt
File "E:\Dev\spot_new\venv\lib\site-packages\flask_bcrypt.py", line 21, in
from werkzeug.security import safe_str_cmp
ImportError: cannot import name 'safe_str_cmp' from 'werkzeug.security' (E:\Dev\spot_new\venv\lib\site-packages\werkzeug\security.py)

i’ve tried uninstalling python, anaconda, pycharm, deleting every reg key and environment variable i can find that looks pythonic, reinstalling all from scratch but still no dice.

any input greatly appreciated!

thanks

Answer 1:

Werkzeug released v2.1.0 today, removing werkzeug.security.safe_str_cmp.

You can probably resolve this issue by pinning Werkzeug~=2.0.0 in your requirements.txt file (or similar).

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Deprecation notice: ReactDOM.render is no longer supported in React 18

I get this error every time I create a new React App and I don’t know how to fix it:

Warning: ReactDOM.render is no longer supported in React 18. Use createRoot instead. Until you switch to the new API, your app will behave as if it’s running React 17. Learn more: https://reactjs.org/link/switch-to-createroot

I created my react app using: npx create-react-app my-app

Answer:

React 18 shipped yesterday (March 29th). ReactDOM.render has been deprecated in React18 and currently issues a warning and runs in a compatible mode.

Deprecations

Deprecations

  • react-dom: ReactDOM.render has been deprecated. Using it will warn and run your app in React 17 mode.
  • react-dom: ReactDOM.hydrate has been deprecated. Using it will warn and run your app in React 17 mode.
  • react-dom: ReactDOM.unmountComponentAtNode has been deprecated.
  • react-dom: ReactDOM.renderSubtreeIntoContainer has been deprecated.
  • react-dom/server: ReactDOMServer.renderToNodeStream has been deprecated

To resolve you can either revert to a previous version of React or update your index.js file to align with the React 18 syntax.
Example:

import { StrictMode } from "react";
import { createRoot } from "react-dom/client";

import App from "./App";

const rootElement = document.getElementById("root");
const root = createRoot(rootElement);

root.render(

);

Capel Flame-Keshan Ivory 18 x 18 Sample Rectangle Machine Woven Rug