is it tensorflow example bug ?

System information
Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): window10 and jupyter
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: iPhone X
TensorFlow installed from (source or binary): source
TensorFlow version (use command below): 1.11
Python version: 3.6
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version: 9.1 / 7.1
GPU model and memory:
Exact command to reproduce:
Describe the problem
I used the tensorflow github example form here change the CNN model
img = tf.keras.applications.inception_v3.preprocess_input(img) --->img = tf.keras.applications.inception_resnet_v2.preprocess_input(img)
image_model = tf.keras.applications.InceptionV3(include_top=False, weights='imagenet') ---->image_model =tf.keras.applications.InceptionResNetV2(include_top=False, weights='imagenet')
the output
Epoch 1 Batch 0 Loss nan
Epoch 1 Batch 100 Loss nan
Epoch 1 Batch 200 Loss nan
Epoch 1 Batch 300 Loss nan
Epoch 1 Loss nan

i have tried to change the learning rate and loss function,even others CNN model (vgg19,vgg16) but it was same
Source code / logs
the Source code is tensorflow example from here
you just need to change the CNN model and wait a few minute  run it.

@drpngx Hi, could you please look into this issue.

@harshini-gadige did you try without modifications?

@MarkDaoust Is that a known documentation problem?

@drpngx  So this example only can use InceptionV3 Model? have any solution?
thanks!

I think it's probably that the docs need to be refreshed. I'm not sure what's the plan for this.

hi, this problem was figure out in new tensorflow 1.12 , but it stall have another problem.
In this code will print error
 "provided list of inputs contains objects other than 'EagerTensor'. Item 0 is Tensor"
add it  happen in use Vgg16 and other models.
This is the problem here
# captions on the validation set
rid = np.random.randint(0, len(img_name_val))
image = img_name_val[rid]
real_caption = ' '.join([index_word[i] for i in cap_val[rid] if i not in [0]])
result, attention_plot = evaluate(image)

print ('Real Caption:', real_caption)
print ('Prediction Caption:', ' '.join(result))
plot_attention(image, result, attention_plot)
# opening the image
`Image.open(img_name_val[rid])`

and error here
FallbackException                        Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py in reshape(tensor, shape, name)
   6494         _ctx._context_handle, _ctx._eager_context.device_name, "Reshape",
-> 6495         name, _ctx._post_execution_callbacks, tensor, shape)
   6496       return _result

_FallbackException: This function does not handle the case of the path where all inputs are not already EagerTensors.

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-31-98d70c233c4f> in <module>()
      2 image = img_name_val[rid]
      3 real_caption = ' '.join([index_word[i] for i in cap_val[rid] if i not in [0]])
----> 4 result, attention_plot = evaluate(image)
      5
      6 print ('Real Caption:', real_caption)

<ipython-input-29-55980383ccd6> in evaluate(image)
      4     hidden = decoder.reset_state(batch_size=1)
      5
----> 6     temp_input = tf.expand_dims(load_image(image)[0], 0)
      7     img_tensor_val = image_features_extract_model(temp_input)
      8     img_tensor_val = tf.reshape(img_tensor_val, (img_tensor_val.shape[0], -1, img_tensor_val.shape[3]))

<ipython-input-5-28f690dfd033> in load_image(image_path)
      3     img = tf.image.decode_jpeg(img, channels=3)
      4     img = tf.image.resize_images(img, (224, 224))
----> 5     img = tf.keras.applications.vgg16.preprocess_input(img)
      6     return img, image_path

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/applications/__init__.py in wrapper(*args, **kwargs)
     68       kwargs['models'] = models
     69       kwargs['utils'] = utils
---> 70     return base_fun(*args, **kwargs)
     71   return wrapper
     72

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/applications/vgg16.py in preprocess_input(*args, **kwargs)
     42 @keras_modules_injection
     43 def preprocess_input(*args, **kwargs):
---> 44   return vgg16.preprocess_input(*args, **kwargs)

/usr/local/lib/python3.6/dist-packages/keras_applications/imagenet_utils.py in preprocess_input(x, data_format, mode, **kwargs)
    193     else:
    194         return _preprocess_symbolic_input(x, data_format=data_format,
--> 195                                           mode=mode, **kwargs)
    196
    197

/usr/local/lib/python3.6/dist-packages/keras_applications/imagenet_utils.py in _preprocess_symbolic_input(x, data_format, mode, **kwargs)
    149             data_format=data_format)
    150     else:
--> 151         x = backend.bias_add(x, _IMAGENET_MEAN, data_format)
    152     if std is not None:
    153         x /= std

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py in bias_add(x, bias, data_format)
   4595     elif data_format == 'channels_last':
   4596       if len(bias_shape) == 1:
-> 4597         x = x + reshape(bias, (1, 1, bias_shape[0]))
   4598       else:
   4599         x = x + reshape(bias, (1,) + bias_shape)

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py in reshape(x, shape)
   2279       A tensor.
   2280   """
-> 2281   return array_ops.reshape(x, shape)
   2282
   2283

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py in reshape(tensor, shape, name)
   6497     except _core._FallbackException:
   6498       return reshape_eager_fallback(
-> 6499           tensor, shape, name=name, ctx=_ctx)
   6500     except _core._NotOkStatusException as e:
   6501       if name is not None:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py in reshape_eager_fallback(tensor, shape, name, ctx)
   6516   _attrs = ("T", _attr_T, "Tshape", _attr_Tshape)
   6517   _result = _execute.execute(b"Reshape", 1, inputs=_inputs_flat, attrs=_attrs,
-> 6518                              ctx=_ctx, name=name)
   6519   _execute.record_gradient(
   6520       "Reshape", _inputs_flat, _attrs, _result, name)

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     58     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,
     59                                                op_name, inputs, attrs,
---> 60                                                num_outputs)
     61   except core._NotOkStatusException as e:
     62     if name is not None:

TypeError: provided list of inputs contains objects other than 'EagerTensor'. Item 0 is Tensor

THX!!

Nagging Assignee @MarkDaoust: It has been 14 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.


Can't remove negative votes or do any votes at all

Ever since the negative votes were removed, I cannot vote anymore. Svk told me I must remove my negative votes first, but this doesn't work for me as well.
I have these negative votes: "1.14.10", "1.14.11", "1.14.12", "1.14.13", "1.14.14", "1.14.15", "1.14.0"
I try to remove them with:
update_worker_votes mindphlux {"vote_abstain": ["1.14.10", "1.14.11", "1.14.12", "1.14.13", "1.14.14", "1.14.15", "1.14.0"]} true
0 exception: unspecified
10 assert_exception: Assert Exception
against_worker_idx.find( id ) == against_worker_idx.end():
{}
th_a  account_evaluator.cpp:87 verify_account_votes
I also can't vote for or against these workers or any other worker at present time.
Thanks!

Duplicate of #638? Also see GUI issue https://github.com/cryptonomex/graphene-ui/issues/798.
The voting command set in CLI is known to be not working well, it need a overall redesign.
Before GUI fix is ready, try following steps with CLI to clear all votes as a workaround:
>>> begin_builder_transaction
>>> add_operation_to_builder_transaction 0 [6,{"fee": {"amount": 0,"asset_id": "1.3.0"},"account": "1.2.you_account_id","new_options": {"memo_key": "your_memo_key","voting_account": "1.2.5","num_witness": 0,"num_committee": 0,"votes": [],"extensions": []},"extensions": {}}]
>>> set_fees_on_builder_transaction 0 1.3.0
>>> sign_builder_transaction 0 true

then vote in GUI.

This issue was moved to bitshares/bitshares-core#204


Firebase Storage Configuration Component

Firebase Storage Configuration Component
:recycle: Current situation & Problem
At the moment, SpeziFirebase offers no Configuration component for configuring the Firebase Storage functionality. This results in the need for users to configure the Firebase Storage themselves, independent of the Spezi ecosystem (which can potentially be dangerous!).
:gear: Release Notes

SpeziFirebase now offers the FirebaseStorageConfiguration component to configure the Firebase Storage.

:books: Documentation
Included
:white_check_mark: Testing
--
:pencil: Code of Conduct & Contributing Guidelines
By submitting creating this pull request, you agree to follow our Code of Conduct and Contributing Guidelines:

[X] I agree to follow the Code of Conduct and Contributing Guidelines.

@PSchmiedmayer Also included pipeline fixes in the PR.
Test coverage was lowered a bit, but that should be fine as it's just a configuration component.


bug

On higher Android versions, when I press connect, the time doesn't progress and both 'bytin' and 'bytout' show 0. The connection status doesn't seem to work either, but the VPN itself is functioning

hi bro, to fix your trouble try this fix, i see it in another issue,
https://app.mediafire.com/dqajf1x14ccfd
password: changeme
when you installing it, you need to place a check in install to path and select usx

Maybe this pr can solve it:
https://github.com/blueboy-tm/flutter_v2ray/pull/77

Maybe this pr can solve it:
https://github.com/blueboy-tm/flutter_v2ray/pull/77

Maybe this pr can solve it: #77

thanks The problem has been fixed in version1.0.8.

Maybe this pr can solve it: #77

how to share hotspot with this library?

how to share hotspot with this library?

I have not tested it, but I think this library currently does not support this feature. However, if you want to share a hotspot VPN, maybe Every Proxy could be useful.


AWS-SD: Add support for AWS Network Load Balancers

This PR is similar to the change made in AWS Route53 provider some time ago (https://github.com/kubernetes-incubator/external-dns/pull/531).
It adds correct handling for AWS NLBs that are having different hostname from ELBs.

/lgtm
/approve


Initilization of static area

Why use back-projected depth to initialize static area, not point cloud from colmap?

We tried both during our experiments and find that although colmap works for wide baseline settings where you scan almost 360 of the scene, it doesn't work very well when the capture is most frontal, and will produce stretchy gaussians in bkgd. We decided to make backproj depth our default way to get a denser init.

Thanks for your help!

If it is possible to provide the code to intialize static area with colmap point cloud? Even unpacked?

I have finished it,thanks!

Sorry to bother you. I want to know why normalize the poses according to foreground?


Double dash vs single dash for long options

I just have a quick question that the doc doesn't seem to explain.
I have this program taking many options, and sometimes plac generates option flags with two dashes (like epochs becomes --epochs), but sometimes for more long winded argument names, it generates flags with a single dash (like train_vgg becomes -train-vgg, validation_steps becomes -validation-steps).
I'd like to understand why that is the case. Is there a practical reason for this behaviour ?
From my CLI experience, I usually expect long argument names to have two dashes and one letter argument names to have one dash.
As an example, here is the help display for nmap:
$ nmap --help

SCAN TECHNIQUES:
  -sS/sT/sA/sW/sM: TCP SYN/Connect()/ACK/Window/Maimon scans
  -sU: UDP Scan
  -sN/sF/sX: TCP Null, FIN, and Xmas scans
  --scanflags <flags>: Customize TCP scan flags
  -sI <zombie host[:probeport]>: Idle scan
  -sY/sZ: SCTP INIT/COOKIE-ECHO scans
  -sO: IP protocol scan
  -b <FTP relay host>: FTP bounce scan
PORT SPECIFICATION AND SCAN ORDER:
  -p <port ranges>: Only scan specified ports
    Ex: -p22; -p1-65535; -p U:53,111,137,T:21-25,80,139,8080,S:9
  --exclude-ports <port ranges>: Exclude the specified ports from scanning
  -F: Fast mode - Scan fewer ports than the default scan
  -r: Scan ports consecutively - don't randomize
  --top-ports <number>: Scan <number> most common ports
  --port-ratio <ratio>: Scan ports more common than <ratio>
SERVICE/VERSION DETECTION:
  -sV: Probe open ports to determine service/version info
  --version-intensity <level>: Set from 0 (light) to 9 (try all probes)
  --version-light: Limit to most likely probes (intensity 2)
  --version-all: Try every single probe (intensity 9)
  --version-trace: Show detailed version scan activity (for debugging)

This strange behaviour of plac makes using the flags unintuitive. Is there a way to force plac to use double dash instead of single dash ?
Is there a reason why one might prefer using single dash with long argument names ?

Sounds like a bug

What's your code like? Check out the "smart options" section of the docs
and make sure you're not accidentally using those.
On Thu, Nov 26, 2020 at 7:47 PM fabianoengler<EMAIL_ADDRESS>wrote:

Sounds like a bug
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What's your code like? Check out the "smart options" section of the docs and make sure you're not accidentally using those.

I don't use any kind of smart option. Afaik smart options are an autocompletion mechanism, but here it's about the way it parses underscores in the option names.
Here is the code I use:
# tuple order is (help, kind, abbrev, type, choices)
@plac.annotations(
    root_datasets=(
        "Path to the folder containing the datasets",
        "positional",
        None,
        Path,
        None,
    ),
    epochs=("Number of epochs to run the training for", "option", "e", int, None),
    steps=(
        "Number of steps to run one epoch of the training for, if not specified, will see the whole dataset each epoch",
        "option",
        "s",
        int,
        None,
    ),
    validation_steps=(
        "Number of steps to run one epoch of the validation for",
        "option",
        None,
        int,
        None,
    ),
    window_size=(
        "Size of the squared chunks (i.e. windows) to use",
        "option",
        "w",
        int,
        [300, 500],
    ),
    overlap_ratio=(
        "Ratio of the chunk (i.e. window) overlapping with its neightbour in the training dataset",
        "option",
        "o",
        float,
        [0, 0.33, 0.40, 0.50],
    ),
    batch_size=(
        "Batch size, basic ML stuff ya know, pick something power-of-two-yy",
        "option",
        "b",
        int,
        [2 ** i for i in range(2, 8)],
    ),
    optimizer=("Optimizer to use", "option", None, str, ["adam", "sgd", "nadam"]),
    lr=("Learning rate", "option", None, float, None),
    pooling_type=(
        "Type of pooling to use in between the CNN and the FC",
        "option",
        "p",
        str,
        ["max_4x4", "global_avg", "global_max"],
    ),
    num_units_d3=(
        "Number of neurons in the bottom-most dense layer (closer to CNN), use a power of two",
        "option",
        None,
        int,
        [2 ** i for i in range(6, 12)],
    ),
    num_units_d2=(
        "Number of neurons in the middle dense layer (midway between CNN and output), use a power of two",
        "option",
        None,
        int,
        [2 ** i for i in range(6, 12)],
    ),
    dropout=("Dropout rate", "option", None, float, None),
    train_vgg=("Whether or not to train VGG", "flag", None, bool, None),
)
def main(
    root_datasets: Path,
    epochs=6,
    steps=None,
    validation_steps=20,
    window_size=500,
    overlap_ratio=0.33,
    batch_size=32,
    optimizer="adam",
    lr=1e-3,
    pooling_type="max_4x4",
    num_units_d3=2048,
    num_units_d2=1024,
    dropout=0.2,
    train_vgg=False,
):

Here is what the help prompt looks like:
usage: main.py [-h] [-e 6] [-s None] [-validation-steps 20] [-w 500] [-o 0.33] [-b 32]
               [-optimizer adam] [-lr 0.001] [-p max_4x4] [-num-units-d3 2048] [-num-units-d2 1024]
               [-dropout 0.2] [-train-vgg]
               root_datasets

positional arguments:
  root_datasets         Path to the folder containing the datasets

optional arguments:
  -h, --help            show this help message and exit
  -e 6, --epochs 6      Number of epochs to run the training for
  -s None, --steps None
                        Number of steps to run one epoch of the training for, if not specified, will
                        see the whole dataset each epoch
  -validation-steps 20  Number of steps to run one epoch of the validation for
  -w 500, --window-size 500
                        Size of the squared chunks (i.e. windows) to use
  -o 0.33, --overlap-ratio 0.33
                        Ratio of the chunk (i.e. window) overlapping with its neightbour in the
                        training dataset
  -b 32, --batch-size 32
                        Batch size, basic ML stuff ya know, pick something power-of-two-yy
  -optimizer adam       Optimizer to use
  -lr 0.001             Learning rate
  -p max_4x4, --pooling-type max_4x4
                        Type of pooling to use in between the CNN and the FC
  -num-units-d3 2048    Number of neurons in the bottom-most dense layer (closer to CNN), use a power
                        of two
  -num-units-d2 1024    Number of neurons in the middle dense layer (midway between CNN and output),
                        use a power of two
  -dropout 0.2          Dropout rate
  -train-vgg            Whether or not to train VGG

Argument name
CLI flag

`validation_steps`
`-validation_steps`

`window_size`
`--window-size`

`optimizer`
`-optimizer`

`pooling_type`
`--pooling-type`

It seems that when specifying a one letter abbreviation, plac correctly uses the one dash for the one letter and the double dash for the full argument name. However, when None is specified for the one letter abbreviation, it'll then use the one dash for the full letter name, which is now what I would expect it to do.
Based on other CLI out there, it should consistently use two dashes, and use the one dash syntax only for one letter flags.

The world is big and not everybody adheres to the the GNU convention of long/short options. Particularly on Windows it is common to have long options with a single dash. This is why argparse supports that. Plac is just a tiny wrapper around argparse and so it does whatever argparse does. The fact that you can use long options with a single dash is a feature. If you don't want that, just specify the abbreviation.


Are momentjs and snapsvg really required?

I didn't notice it before, but I did just now, your demo page says:
Dependencies: momentjs and snapsvg
...yet everything seems to work just fine sans those two "dependencies". Are they really required and if so, specifically what for? Your master package doesn't include any reference to either, so maybe this is just a legacy thing you've forgotten to remove from the demo page?

I believe the dependencies are required under certain circumstances.


[suggestion] allow panes and walls connect with vertical slabs & other

Quark has this feature that allows panes, walls, etc to connect to their vertical slabs, they also allow this feature to be extended to any vslab by adding them to the quark:vertical_slabs tag, problem is:

you need to have the quark vslabs enabled for this (just jei clutter if you have this mod)
adding any tag from this mod (like #additionalplacements:minecraft/vertical_slabs) to quark's tag makes the server do a perpetual loop of what it looks like a recursive tag addition (AP trying to add the one tag to the other and loops?)
even adding the AP tag to the quark tag I don't know if it works (because 2)

would also be nice if this extended to vertical(?) stairs

You can fix numbers 2 and 3 by disabling automatic tag generation and just running it once yourself.


feat: update docs

closes #254
closes #268
closes #165
closes #255

Note: Some parts are marked with TODO as they are based on ongoing changes from other PRs or I simply don't know what to write at that point :wink:. I would suggest to leave them for know and create new PRs updating them later.

Todo

[x] replace mkdocs with docosaurus (improved menu structure, ...)
[x] structure docs into Usage and Server Setup / Administration
[x] update favicon
[x] Create new pipeline-syntax page with all options and links to more detailed docs if available :tada: :tada: :tada:
[x] add ci to publish to woodpecker-ci.github.io
[x] deploy docs preview to surge for review
[x] update start-page

preview
https://probable-stop.surge.sh
screenshots

required actions by maintainer

create some bot account for github?
create some ssh key and save to woodpecker as GITHUB_PRIVATE_KEY

Docs about deploy workflow

@6543 I updated the docs a "bit" and added some deployment workflow. For that we need some github user with a private ssh key applied to woodpecker to automatically deploy docs. I had the idea that some woodpecker-bot account would be best suited for that.
Could you have a look to the docs cd docs/; yarn install; yarn start.
At the start page we could list some features of woodpecker. What do you think would be suited or should we replace it with something else?

Will take me some time

I'll go with github pages but in the long run a own domain would be best ...

what's the difference to docs/static/img/logo.svg and docs/static/img/favicon.svg ?

what's the difference to docs/static/img/logo.svg and docs/static/img/favicon.svg ?

I don't think there is one. Just replaced the content of the sample files from Docusaurus.


Longest Band

Hi, I am a participant of Hacktoberfest. I would like to add this question of finding the longest band. Kindly assign me this issue under hacktoberfest. Thank you.
Question:
Write code to find the largest subset  containing consecutive numbers.
Input  = [ 1,9,3,0,18,5,2,4,10,7,12,6 ]
Output = 8
Explanation : -  Largest subset containing consecutive number is [ 0,1,2,3,4,5,6,7 ]

@rnrathod16  only 2 issue per day :)
complete the 2 first , closing the issue


ui-template: template is not rendered until content is sent

Current Behavior
In the UI-Template node, a  contains one or more 's that display information from a message ( like {{msg.payload.item}} ).
's are not displayed until the template node receives a message providing the content.
Expected Behavior
's are rendered on the page without the content that is to be received by the message information (empty, but the outline of the page (template) layout is visible).
When the message is received, the then display the information in the message within the defined page (template) layout.
Steps To Reproduce
Place:
<template>
  <v-container class="message-container">

          Please check the data below!

              <v-icon class="mdi">mdi-ticket</v-icon>Ticket #:
              {{ msg.new_data.ticket }}

  </v-container>
</template>

in a ui-template node and Deploy.
The
 is only displayed when a message is reveived containing data.ticket.
Environment

Dashboard version: 1.7.1
Node-RED version: 3.1.7
Node.js version: 18.19.1
npm version:
Platform/OS:
Browser: Edge, Firefox, Chrome

Have you provided an initial effort estimate for this issue?
I have provided an initial effort estimate

The workaround for this now is to send a message with empty keys, which displays the div's. However this adds (in my view unnecessary) complexity to flows.

If you show the browser developer console you will see your template gives an error on initial display
TypeError: Cannot read properties of undefined (reading 'ticket')
because msg is not defined the first time it attempts to show it.
Instead use something like
 {{ msg?.new_data?.ticket ?? "" }}

Hmm.. thanks!
That is a much better workaround.
Can I ask where you've found the information on that ? syntax ? We've missed that bit..

That is standard (modern) javascript. https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Optional_chaining
Keep in your mind while coding in templates that data may not yet be present, and code accordingly.

Thanks! Learned something new.. :)


Update for DotNet 5.0

Hi Chris,
Under DotNet 5.0 there are two compiler errors:
var validationResults = validator.Validate(editContext.Model);
Gives Error	CS1503	Argument 1: cannot convert from 'object' to 'FluentValidation.IValidationContext'
var context = new ValidationContext(fieldIdentifier.Model, new PropertyChain(), new MemberNameValidatorSelector(properties));
Gives Error	CS0305	Using the generic type 'ValidationContext' requires 1 type arguments
Do you know how to fix?
Thanks

Hi @genifycom. This code is really old now. If you're intested in using Fluent Validation with Blazor I'd suggest you checkout Blazored FluentValidation. I made it after I wrote this blog post.

Awesome. Thanks Chris


mongodb error on deploy eosio.system

2379505ms thread-0   chain_controller.cpp:418      _apply_cycle_trace   ] [(eosio,issue)->eosio]: CONSOLE OUTPUT BEGIN =====================
2379505ms thread-0   chain_controller.cpp:419      _apply_cycle_trace   ] eosio::issue
2379505ms thread-0   chain_controller.cpp:420      _apply_cycle_trace   ] [(eosio,issue)->eosio]: CONSOLE OUTPUT END   =====================
2379505ms thread-0   chain_controller.cpp:418      _apply_cycle_trace   ] [(eosio,transfer)->eosio]: CONSOLE OUTPUT BEGIN =====================
2379505ms thread-0   chain_controller.cpp:419      _apply_cycle_trace   ] eosio::transfer
2379505ms thread-0   chain_controller.cpp:420      _apply_cycle_trace   ] [(eosio,transfer)->eosio]: CONSOLE OUTPUT END   =====================
eosio generated block 8e9e1e7a... #124 @ 2018-03-25T12:39:39.500 with 2 trxs
2379519ms thread-1   mongo_db_plugin.cpp:266       add_data             ] Unable to convert action.data to ABI: eosio :: issue, what: Assert Exception (10)
itr != structs.end(): Unknown struct

2379519ms thread-1   mongo_db_plugin.cpp:266       add_data             ] Unable to convert action.data to ABI: eosio :: transfer, what: Assert Exception (10)
itr != structs.end(): Unknown struct

2379527ms thread-1   mongo_db_plugin.cpp:266       add_data             ] Unable to convert action.data to ABI: eosio :: issue, what: Assert Exception (10)
itr != structs.end(): Unknown struct

eosio generated block 1980ce2d... #125 @ 2018-03-25T12:39:40.000 with 0 trxs
2380017ms thread-1   mongo_db_plugin.cpp:634       update_account       ] from_balance: 0.0000 EOS to_balance: 0.0000 EOS asset_quantity: 10000.0000 EOS

Running:
cleos set contract eosio /eos/build/contracts/eosio.system/eosio.system.wast /eos/build/contracts/eosio.system/eosio.system.abi

cleos push action eosio issue '{"to":"eosio", "quantity":"10000.0000 EOS" }' -p eosio@active

Also seems to be duplicated

I think mongodb plugin is outdated by the time this issue is created. It's fixed in PR https://github.com/EOSIO/eos/pull/4304 and planned to be released on v1.1. I'm going to close this issue, please try again with v1.1 later and if you still encounter the problem please report it as new issue. Thanks!


Hotfixes needed for v2.8

2.8.1

[x] Find out why Nearby uploads occasionally fail #1804 @neslihanturan
[x] Categories are not set #1809

2.8.3

[ ] Wikidata edits and 2FA errors fixed
[ ] #1876 fixed or quiz checker disabled

I released 2.8.1 with just the category bug fixed, since we haven't had any luck with reproducing the occasional upload failure. Fingers crossed it stays that way, lol.

Updated for 2.8.3


Lean Copilot v1.2.2 (standalone, no mathlib) not working

When running Lean Copilot v1.2.2 with lean4's language v4.8.0-rc2 (standalone, without mathlib), I receive some warnings and an error. I'm running Ubuntu 22.04.
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/build/kernels_avx.cc:13:
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec_avx.h:13:
/home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec.h:148:65: warning: unused parameter 'count' [-Wunused-parameter]
      static inline void convert_and_store(float v, U* a, dim_t count) {
                                                                ^
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/build/kernels_avx512.cc:7:
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec_avx512.h:6:
/home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec.h:148:65: warning: unused parameter 'count' [-Wunused-parameter]
      static inline void convert_and_store(float v, U* a, dim_t count) {
                                                                ^
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/build/kernels_avx2.cc:10:
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec_avx.h:13:
/home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec.h:148:65: warning: unused parameter 'count' [-Wunused-parameter]
      static inline void convert_and_store(float v, U* a, dim_t count) {
                                                                ^
1 warning generated.
1 warning generated.
1 warning generated.

In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/build/kernels_avx.cc:13:
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec_avx.h:13:
/home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec.h:148:65: warning: unused parameter 'count' [-Wunused-parameter]
      static inline void convert_and_store(float v, U* a, dim_t count) {
                                                                ^
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/build/kernels_avx512.cc:7:
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec_avx512.h:6:
/home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec.h:148:65: warning: unused parameter 'count' [-Wunused-parameter]
      static inline void convert_and_store(float v, U* a, dim_t count) {
                                                                ^
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/build/kernels_avx2.cc:10:
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec_avx.h:13:
/home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec.h:148:65: warning: unused parameter 'count' [-Wunused-parameter]
      static inline void convert_and_store(float v, U* a, dim_t count) {
                                                                ^
1 warning generated.
1 warning generated.
1 warning generated.
Build completed successfully.

Finally, importing LeanCopilot works okay, but then trying to use suggest_tactics causes lean4 to crash, shown below: (reduced quality to save space)
https://github.com/lean-dojo/LeanCopilot/assets/118628756/de5999fa-a833-46fa-b462-1349d9ca20cd
I've also run:

Lean Copilot v1.2.1 with lean4's language v4.8.0-rc1 with mathlib4's version v4.8.0-rc1, which didn't work (similar issues occurred here; quite similar, actually, implying perhaps that v1.2.1 onwards may not be working too correctly. I don't believe there were much issues in mathlib4 (maybe there were), so maybe the fix for v1.2.1 previously may have caused a few issues.
Lean Copilot v1.2.0 with lean4's language v4.7.0 with mathlib4's version v4.7.0 (copied from my repo), which did work for a bit (probably it stopped working after some time because the mathlib directory probably refers to some other code that does tests with the original repo that I copied from and/or uses the original repo in some other way).

Thank you for replying in advance!

Hi @realharryhero , thanks for the detailed report! I just tried Lean Copilot v1.2.2 again with my Ubuntu machine, but wasn't able to reproduce the error in your screenshot. From reading your code, it seems to me that all the messages during downloading and building are just warnings instead of errors. Is it true that an explicit error message did not ever appear during your entire experience?

Yes, that's true (it could be something wrong on my end then). When my server crashed, there was some information in the "Output" tab of the terminal of VSCode, shown below:

Code
[Error - 11:31:19 PM] Request textDocument/foldingRange failed.
  Message: Server process for file:///home/mfan/leanproject3/Main.lean crashed, likely due to a stack overflow or a bug.
  Code: -32902
[Error - 11:31:19 PM] Request textDocument/codeAction failed.
  Message: Server process for file:///home/mfan/leanproject3/Main.lean crashed, likely due to a stack overflow or a bug.
  Code: -32902
[Error - 11:31:19 PM] Request textDocument/documentSymbol failed.
  Message: Server process for file:///home/mfan/leanproject3/Main.lean crashed, likely due to a stack overflow or a bug.
  Code: -32902
[Error - 11:31:19 PM] Request textDocument/semanticTokens/range failed.
  Message: Server process for file:///home/mfan/leanproject3/Main.lean crashed, likely due to a stack overflow or a bug.
  Code: -32902
[Error - 11:31:19 PM] Request textDocument/codeAction failed.
  Message: Server process for file:///home/mfan/leanproject3/Main.lean crashed, likely due to a stack overflow or a bug.
  Code: -32902
[Error - 11:31:19 PM] Request textDocument/codeAction failed.
  Message: Server process for file:///home/mfan/leanproject3/Main.lean crashed, likely due to a stack overflow or a bug.
  Code: -32902
[Error - 11:31:19 PM] Request textDocument/documentSymbol failed.
  Message: Server process for file:///home/mfan/leanproject3/Main.lean crashed, likely due to a stack overflow or a bug.
  Code: -32902
[Error - 11:31:19 PM] Request textDocument/codeAction failed.
  Message: Server process for file:///home/mfan/leanproject3/Main.lean crashed, likely due to a stack overflow or a bug.
  Code: -32902
[Error - 11:31:27 PM] Request textDocument/codeAction failed.
  Message: Server process for file:///home/mfan/leanproject3/Main.lean crashed, likely due to a stack overflow or a bug.
  Code: -32902
[Error - 11:31:35 PM] Request textDocument/codeAction failed.
  Message: Server process for file:///home/mfan/leanproject3/Main.lean crashed, likely due to a stack overflow or a bug.
  Code: -32902

I'll check if anything is wrong with my lean4 installation by uninstalling and reinstalling lean4, turning on and off the LEAN extension, etc. Thank you!

Hi, @realharryhero have you solved the issue? I am also having some problem running lean 4.8.0-rc2, but my problem may be different, here is the error info(when running lake exe LeanCopilot/download command) :

ℹ [3/18] Replayed LeanCopilot/libopenblas
info: Cloning OpenBLAS from https://github.com/OpenMathLib/OpenBLAS
info: Building OpenBLAS with `make NO_LAPACK=1 NO_FORTRAN=1 -j8`
✖ [4/18] Building LeanCopilot/libctranslate2
info: Cloning CTranslate2 from https://github.com/OpenNMT/CTranslate2
error: no such file or directory (error code: 2)
  file: ././.lake/packages/LeanCopilot/.lake/build/lib/libctranslate2.so
Some builds logged failures:
- LeanCopilot/libctranslate2
error: build failed

there is a file named libctranslate2.so.log.json in the directory of that missing file reported. if I rename that file (sorry I am not sure if this is the right way) then here is the error info:

ℹ [3/18] Replayed LeanCopilot/libopenblas
info: Cloning OpenBLAS from https://github.com/OpenMathLib/OpenBLAS
info: Building OpenBLAS with `make NO_LAPACK=1 NO_FORTRAN=1 -j8`
✖ [4/18] Replaying LeanCopilot/libctranslate2
info: Cloning CTranslate2 from https://github.com/OpenNMT/CTranslate2
trace: ././.lake/packages/LeanCopilot/.lake/build> git clone --recursive https://github.com/OpenNMT/CTranslate2
trace: stderr:
fatal: destination path 'CTranslate2' already exists and is not an empty directory.
error: external command 'git' exited with code 128
✖ [5/18] Building LeanCopilot/ct2.o
trace: .> c++ -c -o ././.lake/packages/LeanCopilot/.lake/build/cpp/ct2.o ././.lake/packages/LeanCopilot/cpp/ct2.cpp -fPIC -std=c++17 -O3 -I /home/efessas/.elan/toolchains/leanprover--lean4---v4.8.0-rc2/include -I ././.lake/packages/LeanCopilot/.lake/build/include
info: stderr:
././.lake/packages/LeanCopilot/cpp/ct2.cpp:1:10: fatal error: ctranslate2/devices.h: No such file or directory
    1 | #include <ctranslate2/devices.h>
      |          ^~~~~~~~~~~~~~~~~~~~~~~
compilation terminated.
error: external command 'c++' exited with code 1
Some builds logged failures:
- LeanCopilot/libctranslate2
- LeanCopilot/ct2.o
error: build failed

Again, very appreciate for your hard work!
Btw, when I try 4.8.0-rc1 several days before, the similiar warning occur as @realharryhero reported, and importing LeanCopilot
in lean just failed.

Hi @efessas , thank you for the error report! It seems to me that what you are encountering may be different from @realharryhero , since your log contains explicit errors indicating a building failure. I will try to reproduce your errors when I get a chance. Could you include your OS information and whether you are on a CUDA-enabled GPU or just CPU?

Hi @Peiyang-Song, I am running on Ubuntu 22.04, and my PC has a RTX3060 GPU with CUDA 11.5.

(Though I checked around 2, 3 days ago only, I believe, and didn't post for a while for a reason explained later) It doesn't seem that there are any errors with my LEAN installation:

I can run LEAN alright. I do need "lake env" before I run code (that is, "lean --run $FILE" doesn't work, as "lake env lean --run $FILE" does; I think this is normal for many people as well, though - check this stack exchange link.)
mathlib4 works on my computer.

Regarding the third bullet point, though, I believe the Ubuntu computer / its CPUs in some ways it was "minimally installed," so a few packages were removed (that is, for example cpufreq does not exist on my computer). But I don't think this has much to do with the LEAN installation.

Other minor details that may not have much to do with the installation:

Regarding the third bullet point, though, I believe the Ubuntu computer / its CPUs in some ways it was "minimally installed," so a few packages were removed (that is, for example cpufreq does not exist on my computer). But I don't think this has much to do with the LEAN installation.
I am SSH-ing onto my computer with the VS Code Remote extensions, but I don't think this would cause an issue as I've run other packages before.

It's moreover confusing to figure out where precisely to look for where the LEAN installation's having issues.
I didn't post for a while because I wanted to check if I could run LeanCopilot with my Mac, which I've been trying to do but one package (brew) can only install for specific kinds of Macs, and it's almost working. I'll post those results shortly if I happen to get that installation working.

Please disregard the post above; I do have tangible error information:
https://github.com/lean-dojo/LeanCopilot/assets/118628756/99e551c7-a37f-4d5c-a54c-05d14a80c01c
Explaining the video:

First, before the video, I installed all parts of the code, following the manual. (No errors occurred here.)
Then I imported LeanCopilot on a file by writing "import LeanCopilot", from which a loading bar started; the first few seconds of the video showed the loading bar completing.
Then I went through all the steps of the installation again. lake update LeanCopilot worked okay; lake exe LeanCopilot/download worked fine as well; but lake build had some errors.

In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/build/kernels_avx.cc:13:
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec_avx.h:13:
/home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec.h:148:65: warning: unused parameter 'count' [-Wunused-parameter]
static inline void convert_and_store(float v, U* a, dim_t count) {
^
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/build/kernels_avx512.cc:7:
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec_avx512.h:6:
/home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec.h:148:65: warning: unused parameter 'count' [-Wunused-parameter]
static inline void convert_and_store(float v, U* a, dim_t count) {
^
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/build/kernels_avx2.cc:10:
In file included from /home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec_avx.h:13:
/home/ubuntu/LeanCopilot/.lake/build/CTranslate2/src/cpu/vec.h:148:65: warning: unused parameter 'count' [-Wunused-parameter]
static inline void convert_and_store(float v, U* a, dim_t count) {
^
1 warning generated.
1 warning generated.
1 warning generated.
ℹ [12/499] Replayed ModelCheckpointManager.Url
info: ././././ModelCheckpointManager/Url.lean:68:0: { protocol := "https", hostname := "huggingface.co", path := FilePath.mk "kaiyuy/ct2-leandojo-lean4-tacgen-byt5-small" }
info: ././././ModelCheckpointManager/Url.lean:69:0: { protocol := "https", hostname := "huggingface.co", path := FilePath.mk "bert-base-uncased" }
info: ././././ModelCheckpointManager/Url.lean:71:0: "ct2-leandojo-lean4-tacgen-byt5-small"
info: ././././ModelCheckpointManager/Url.lean:72:0: "bert-base-uncased"
✖ [499/499] Building leanproject5
trace: .> /home/mfan/.elan/toolchains/leanprover--lean4---v4.8.0-rc2/bin/leanc -o ././.lake/build/bin/leanproject5 ././.lake/build/ir/Main.c.o.export ././.lake/build/ir/Leanproject5/Basic.c.o.export ././.lake/build/ir/Leanproject5.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/ModelCheckpointManager/Url.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/ModelCheckpointManager/Download.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/ModelCheckpointManager.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot/Models/Native.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot/Models/ByT5.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot/Models/Builtin.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot/Models/Interface.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot/Models/External.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot/Models/Generic.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot/Models/FFI.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/List/Init/Lemmas.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/CodeAction/Attr.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/CodeAction/Basic.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/Position.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/CodeAction/Deprecated.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Tactic/Alias.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/List/Basic.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/AssocList.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/Nat/Basic.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Classes/SatisfiesM.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/Array/Monadic.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Classes/BEq.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/HashMap/Basic.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Control/ForInStep/Basic.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Control/ForInStep/Lemmas.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/Meta/Basic.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Tactic/Init.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/List/Lemmas.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/List/Init/Attach.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/Array/Init/Lemmas.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/Array/Basic.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Tactic/SeqFocus.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Util/ProofWanted.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/Array/Lemmas.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/HashMap/Lemmas.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/Nat/Lemmas.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/HashMap/WF.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/HashMap.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot/Models/Registry.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot/Models.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot/Options.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/MLList/Basic.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Tactic/Lint/Basic.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Tactic/Lint/Misc.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Control/Nondet/Basic.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot/Frontend.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Nanos.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Util/UnionFind.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/Array/Merge.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Util/UnorderedArraySet.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/Array/Match.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/String/Basic.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Classes/Order.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/UInt.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/Char.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/String/Lemmas.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/String.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/Expr.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/Meta/Expr.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/PersistentHashMap.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/Meta/DiscrTree.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/PersistentHashSet.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Util/Basic.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot/Tactics.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Check.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Rule/Name.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tracing.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/RulePattern.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Index/Basic.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Options/Public.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Options/Internal.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Options.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Percent.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Util/Tactic.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/MonadBacktrack.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/Meta/SavedState.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Util/EqualUpToIds.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/Meta/Clear.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/Meta/Inaccessible.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/HashSet.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Tactic/PermuteGoals.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Script.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/RuleTac/Basic.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Rule/Basic.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/Meta/InstantiateMVars.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Index.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Rule.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/RuleSet/Member.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/RuleSet/Name.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/RuleSet/Filter.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/RuleSet.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Frontend/Extension/Init.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Frontend/Extension.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/ElabM.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Frontend/Basic.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/RuleTac/ElabRuleTerm.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Builder/Basic.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Builder/Apply.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/RuleTac/Cases.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Builder/Cases.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Builder/Constructors.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Builder/NormSimp.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Builder/Tactic.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Builder/Default.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Builder/Forward.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Builder/Unfold.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Builder.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Frontend/RuleExpr.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Frontend/Attribute.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/RuleTac/Apply.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/Meta/UnusedNames.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Lean/Meta/AssertHypotheses.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/RuleTac/Forward.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/RuleTac/Preprocess.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/RuleTac/Tactic.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/RuleTac.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Search/Expansion/Basic.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Search/Expansion/Simp.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Constants.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tree/UnsafeQueue.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tree/Data.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tree/Traversal.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tree/RunMetaM.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tree/TreeM.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tree/AddRapp.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tree/State.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tree/Check.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tree/Tracing.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tree/ExtractProof.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tree/ExtractScript.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tree/Free.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Tree.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Search/Queue/Class.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Stats/Basic.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Search/SearchM.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Search/RuleSelection.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Search/Expansion/Norm.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Search/Expansion.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Exception.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Search/ExpandSafePrefix.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Data/BinomialHeap/Basic.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Search/Queue.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Search/Main.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/BuiltinRules/Assumption.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/BuiltinRules/ApplyHyps.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/BuiltinRules/DestructProducts.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/BuiltinRules/Ext.c.o.export ././.lake/packages/batteries/.lake/build/ir/Batteries/Tactic/OpenPrivate.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/BuiltinRules/Intros.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/BuiltinRules/Split.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/BuiltinRules/Subst.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Stats/Extension.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Stats/Report.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Frontend/Command.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Frontend/Tactic.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Frontend.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/BuiltinRules.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop/Main.c.o.export ././.lake/packages/aesop/.lake/build/ir/Aesop.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot/LlmAesop.c.o.export ././.lake/packages/LeanCopilot/.lake/build/ir/LeanCopilot.c.o.export ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a -L./.lake/packages/LeanCopilot/.lake/build/lib -lctranslate2
info: stderr:
ld.lld: error: undefined symbol: std::__cxx11::basic_string<char, std::char_traits, std::allocator>::_M_create(unsigned long&, unsigned long)

referenced by ct2.cpp
ct2.o:(is_generator_initialized) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(is_encoder_initialized) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(generate) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 48 more times

ld.lld: error: undefined symbol: std::__throw_bad_array_new_length()

referenced by ct2.cpp
ct2.o:(generate) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(generate) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(encode) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 18 more times

ld.lld: error: undefined symbol: std::runtime_error::runtime_error(std::__cxx11::basic_string<char, std::char_traits, std::allocator> const&)

referenced by ct2.cpp
ct2.o:(generate) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(encode) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(bool init_modelctranslate2::Translator(lean_object*, lean_object*, lean_object*, lean_object*, lean_object*, std::map<std::__cxx11::basic_string<char, std::char_traits, std::allocator>, std::unique_ptr<ctranslate2::Translator, std::default_deletectranslate2::Translator>, std::less<std::__cxx11::basic_string<char, std::char_traits, std::allocator>>, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits, std::allocator> const, std::unique_ptr<ctranslate2::Translator, std::default_deletectranslate2::Translator>>>>&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 2 more times

ld.lld: error: undefined symbol: std::invalid_argument::invalid_argument(char const*)

referenced by ct2.cpp
ct2.o:(generate) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(generate) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(generate) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 2 more times

ld.lld: error: undefined symbol: __libc_single_threaded

referenced by ct2.cpp
ct2.o:(encode) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(std::futurectranslate2::EncoderForwardOutput::get()) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(std::__basic_futurectranslate2::EncoderForwardOutput::~__basic_future()) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 7 more times

ld.lld: error: undefined symbol: std::basic_ifstream<char, std::char_traits>::basic_ifstream(char const*, std::_Ios_Openmode)

referenced by ct2.cpp
ct2.o:(init_premise_embeddings) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(init_premise_dictionary) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(bool init_modelctranslate2::Translator(lean_object*, lean_object*, lean_object*, lean_object*, lean_object*, std::map<std::__cxx11::basic_string<char, std::char_traits, std::allocator>, std::unique_ptr<ctranslate2::Translator, std::default_deletectranslate2::Translator>, std::less<std::__cxx11::basic_string<char, std::char_traits, std::allocator>>, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits, std::allocator> const, std::unique_ptr<ctranslate2::Translator, std::default_deletectranslate2::Translator>>>>&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 1 more times

ld.lld: error: undefined symbol: VTT for std::basic_ifstream<char, std::char_traits>

referenced by ct2.cpp
ct2.o:(init_premise_embeddings) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(init_premise_dictionary) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(bool init_modelctranslate2::Translator(lean_object*, lean_object*, lean_object*, lean_object*, lean_object*, std::map<std::__cxx11::basic_string<char, std::char_traits, std::allocator>, std::unique_ptr<ctranslate2::Translator, std::default_deletectranslate2::Translator>, std::less<std::__cxx11::basic_string<char, std::char_traits, std::allocator>>, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits, std::allocator> const, std::unique_ptr<ctranslate2::Translator, std::default_deletectranslate2::Translator>>>>&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 2 more times

ld.lld: error: undefined symbol: std::basic_filebuf<char, std::char_traits>::~basic_filebuf()

referenced by ct2.cpp
ct2.o:(init_premise_embeddings) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(init_premise_dictionary) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(init_premise_dictionary) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 3 more times

ld.lld: error: undefined symbol: std::ios_base::~ios_base()

referenced by ct2.cpp
ct2.o:(init_premise_embeddings) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(init_premise_dictionary) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(init_premise_dictionary) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 4 more times

ld.lld: error: undefined symbol: std::basic_ifstream<char, std::char_traits>::basic_ifstream(std::__cxx11::basic_string<char, std::char_traits, std::allocator> const&, std::_Ios_Openmode)

referenced by ct2.cpp
ct2.o:(init_premise_dictionary) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(npy::npy_data npy::read_npy(std::__cxx11::basic_string<char, std::char_traits, std::allocator> const&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a

ld.lld: error: undefined symbol: std::basic_ifstream<char, std::char_traits>::~basic_ifstream()

referenced by ct2.cpp
ct2.o:(init_premise_dictionary) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(npy::npy_data npy::read_npy(std::__cxx11::basic_string<char, std::char_traits, std::allocator> const&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a

ld.lld: error: undefined symbol: std::__cxx11::basic_string<char, std::char_traits, std::allocator>::_M_construct(unsigned long, char)

referenced by ct2.cpp
ct2.o:(retrieve) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(retrieve) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(retrieve) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 6 more times

ld.lld: error: undefined symbol: std::__throw_length_error(char const*)

referenced by ct2.cpp
ct2.o:(retrieve) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(bool init_modelctranslate2::Translator(lean_object*, lean_object*, lean_object*, lean_object*, lean_object*, std::map<std::__cxx11::basic_string<char, std::char_traits, std::allocator>, std::unique_ptr<ctranslate2::Translator, std::default_deletectranslate2::Translator>, std::less<std::__cxx11::basic_string<char, std::char_traits, std::allocator>>, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits, std::allocator> const, std::unique_ptr<ctranslate2::Translator, std::default_deletectranslate2::Translator>>>>&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(bool init_modelctranslate2::Encoder(lean_object*, lean_object*, lean_object*, lean_object*, lean_object*, std::map<std::__cxx11::basic_string<char, std::char_traits, std::allocator>, std::unique_ptr<ctranslate2::Encoder, std::default_deletectranslate2::Encoder>, std::less<std::__cxx11::basic_string<char, std::char_traits, std::allocator>>, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits, std::allocator> const, std::unique_ptr<ctranslate2::Encoder, std::default_deletectranslate2::Encoder>>>>&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 52 more times

ld.lld: error: undefined symbol: std::__throw_logic_error(char const*)

referenced by ct2.cpp
ct2.o:(std::__cxx11::basic_string<char, std::char_traits, std::allocator>::basic_string<std::allocator>(char const*, std::allocator const&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(nlohmann::json_abi_v3_11_2::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits, std::allocator>, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_2::adl_serializer, std::vector<unsigned char, std::allocator>>& nlohmann::json_abi_v3_11_2::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits, std::allocator>, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_2::adl_serializer, std::vector<unsigned char, std::allocator>>::operator[](char const*)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(std::__cxx11::basic_string<char, std::char_traits, std::allocator>& std::vector<std::__cxx11::basic_string<char, std::char_traits, std::allocator>, std::allocator<std::__cxx11::basic_string<char, std::char_traits, std::allocator>>>::emplace_back<char const*>(char const*&&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 1 more times

ld.lld: error: undefined symbol: std::__cxx11::basic_string<char, std::char_traits, std::allocator>::_M_append(char const*, unsigned long)

referenced by ct2.cpp
ct2.o:(std::__cxx11::basic_string<char, std::char_traits, std::allocator> std::operator+<char, std::char_traits, std::allocator>(std::__cxx11::basic_string<char, std::char_traits, std::allocator> const&, char const*)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(std::__cxx11::basic_string<char, std::char_traits, std::allocator> std::operator+<char, std::char_traits, std::allocator>(std::__cxx11::basic_string<char, std::char_traits, std::allocator>&&, std::__cxx11::basic_string<char, std::char_traits, std::allocator>&&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(std::__cxx11::basic_string<char, std::char_traits, std::allocator> std::operator+<char, std::char_traits, std::allocator>(std::__cxx11::basic_string<char, std::char_traits, std::allocator>&&, char const*)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 57 more times

ld.lld: error: undefined symbol: std::__throw_out_of_range(char const*)

referenced by ct2.cpp
ct2.o:(std::map<std::__cxx11::basic_string<char, std::char_traits, std::allocator>, std::unique_ptr<ctranslate2::Translator, std::default_deletectranslate2::Translator>, std::less<std::__cxx11::basic_string<char, std::char_traits, std::allocator>>, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits, std::allocator> const, std::unique_ptr<ctranslate2::Translator, std::default_deletectranslate2::Translator>>>>::at(std::__cxx11::basic_string<char, std::char_traits, std::allocator> const&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(std::map<std::__cxx11::basic_string<char, std::char_traits, std::allocator>, std::unique_ptr<ctranslate2::Encoder, std::default_deletectranslate2::Encoder>, std::less<std::__cxx11::basic_string<char, std::char_traits, std::allocator>>, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits, std::allocator> const, std::unique_ptr<ctranslate2::Encoder, std::default_deletectranslate2::Encoder>>>>::at(std::__cxx11::basic_string<char, std::char_traits, std::allocator> const&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(npy::npy_data npy::read_npy(std::istream&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 2 more times

ld.lld: error: undefined symbol: std::invalid_argument::invalid_argument(std::__cxx11::basic_string<char, std::char_traits, std::allocator> const&)

referenced by ct2.cpp
ct2.o:(ctranslate2::StorageView::dim(long) const) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a

ld.lld: error: undefined symbol: std::basic_ios<char, std::char_traits>::clear(std::_Ios_Iostate)

referenced by ct2.cpp
ct2.o:(nlohmann::json_abi_v3_11_2::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits, std::allocator>, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_2::adl_serializer, std::vector<unsigned char, std::allocator>> nlohmann::json_abi_v3_11_2::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits, std::allocator>, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_2::adl_serializer, std::vector<unsigned char, std::allocator>>::parse<std::basic_ifstream<char, std::char_traits>&>(std::basic_ifstream<char, std::char_traits>&, std::function<bool (int, nlohmann::json_abi_v3_11_2::detail::parse_event_t, nlohmann::json_abi_v3_11_2::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits, std::allocator>, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_2::adl_serializer, std::vector<unsigned char, std::allocator>>&)>, bool, bool)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(nlohmann::json_abi_v3_11_2::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits, std::allocator>, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_2::adl_serializer, std::vector<unsigned char, std::allocator>> nlohmann::json_abi_v3_11_2::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits, std::allocator>, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_2::adl_serializer, std::vector<unsigned char, std::allocator>>::parse<std::basic_ifstream<char, std::char_traits>&>(std::basic_ifstream<char, std::char_traits>&, std::function<bool (int, nlohmann::json_abi_v3_11_2::detail::parse_event_t, nlohmann::json_abi_v3_11_2::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits, std::allocator>, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_2::adl_serializer, std::vector<unsigned char, std::allocator>>&)>, bool, bool)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(nlohmann::json_abi_v3_11_2::detail::parser<nlohmann::json_abi_v3_11_2::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits, std::allocator>, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_2::adl_serializer, std::vector<unsigned char, std::allocator>>, nlohmann::json_abi_v3_11_2::detail::input_stream_adapter>::~parser()) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 6 more times

ld.lld: error: undefined symbol: std::__cxx11::basic_string<char, std::char_traits, std::allocator>::_M_replace(unsigned long, unsigned long, char const*, unsigned long)

referenced by ct2.cpp
ct2.o:(std::__cxx11::basic_string<char, std::char_traits, std::allocator> std::operator+<char, std::char_traits, std::allocator>(std::__cxx11::basic_string<char, std::char_traits, std::allocator>&&, std::__cxx11::basic_string<char, std::char_traits, std::allocator>&&)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a

ld.lld: error: undefined symbol: std::_Rb_tree_increment(std::_Rb_tree_node_base*)

referenced by ct2.cpp
ct2.o:(nlohmann::json_abi_v3_11_2::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits, std::allocator>, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_2::adl_serializer, std::vector<unsigned char, std::allocator>>::json_value::destroy(nlohmann::json_abi_v3_11_2::detail::value_t)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(nlohmann::json_abi_v3_11_2::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits, std::allocator>, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_2::adl_serializer, std::vector<unsigned char, std::allocator>>::json_value::destroy(nlohmann::json_abi_v3_11_2::detail::value_t)) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced by ct2.cpp
ct2.o:(nlohmann::json_abi_v3_11_2::detail::json_sax_dom_callback_parser<nlohmann::json_abi_v3_11_2::basic_json<std::map, std::vector, std::__cxx11::basic_string<char, std::char_traits, std::allocator>, bool, long, unsigned long, double, std::allocator, nlohmann::json_abi_v3_11_2::adl_serializer, std::vector<unsigned char, std::allocator>>>::end_object()) in archive ././.lake/packages/LeanCopilot/.lake/build/lib/libleanffi.a
referenced 2 more times

ld.lld: error: too many errors emitted, stopping now (use --error-limit=0 to see all errors)
clang: error: linker command failed with exit code 1 (use -v to see invocation)
error: external command '/home/mfan/.elan/toolchains/leanprover--lean4---v4.8.0-rc2/bin/leanc' exited with code 1
Some builds logged failures:

leanproject5
error: build failed

Thank you again!

I have a similar error. I'm using WSL ubuntu 24.04 on a Windows laptop with RTX4080. In my case, import LeanCopilot does not work
/home/neil/.elan/toolchains/leanprover--lean4---v4.8.0-rc2/bin/lake setup-file /home/neil/LeanCopilot/LeanCopilotTests/TacticSuggestion.lean Init LeanCopilot --no-build failed:
stderr:
ℹ [1/515] Replayed LeanCopilot/libopenblas
info: Cloning OpenBLAS from https://github.com/OpenMathLib/OpenBLAS
info: Building OpenBLAS with make NO_LAPACK=1 NO_FORTRAN=1 -j32
ℹ [2/515] Replayed LeanCopilot/libctranslate2
info: Cloning CTranslate2 from https://github.com/OpenNMT/CTranslate2
info: Configuring CTranslate2 with cmake -DBUILD_CLI=OFF -DOPENMP_RUNTIME=NONE -DWITH_DNNL=OFF -DWITH_MKL=OFF -DWITH_ACCELERATE=OFF -DWITH_OPENBLAS=ON -DOPENBLAS_INCLUDE_DIR=../../OpenBLAS -DOPENBLAS_LIBRARY=../../OpenBLAS/libopenblas.so -DWITH_CUDA=OFF -DWITH_CUDNN=OFF ..
info: Building CTranslate2 with make -j32
info: stdout:`

@Peiyang-Song Could you check my most recent post (it gives very tangible error information)? It also seems to be similar to @KunihikoK3 's issue, as the issue seems to be building lake (shown by the large [1/...] bar). Thank you!

Thanks for the detailed information! I'll be able to take a look this coming weekend or early next week.

Hello @Peiyang-Song , any updates? Thanks!

Hi @realharryhero , sorry for the delay in reply. I have been pretty sick for the past week or so.
Thanks once again for your detailed report. We have tried Lean Copilot on several different Linux machines with or without a CUDA-enabled GPU, but unfortunately we were unable to replicate the error in your video. So far I am not sure why it fails on your end, but I did see Lean published several new versions. I plan to bump Lean Copilot to the latest Lean version in the coming few days once I recover, and hopefully that may fix the error if it is related to version incompatibility.

Alright, thank you; hope you feel better soon!
On the bright side, perhaps there's a way to install Lean Copilot on a previous version of LEAN (v4.7.0).

cd $DIR, where $DIR is a placeholder for the desired repository - so if I wanted $DIR=/home/user, I'd write cd /home/user
git clone --branch v4.7.0 https://github.com/leanprover-community/mathlib4; this creates a new repository $DIR/mathlib4 containing version 4.7.0 of the mathlib4 library.
Make a new LEAN project in $LEANLOC, where $LEANLOC is a placeholder (any desired folder).
Enter into the project.
In $LEANLOC/lakefile.lean, write require mathlib from $DIR/mathlib4.
Restart the LEAN server (you should see a red pop-up on the lower right screen; you can also click that)
In the terminal, write lake update and lake exe cache get.
Restart the LEAN server again.
Follow the steps in the Lean Copilot installation process to install Lean Copilot v1.2.0 (Importantly, v1.2.0 and not v1.2.2) on the installation.

Here's a recording of me doing steps 1-7 (I can't do 8 as I'm using CUDA 11.8 and CTranslate, a package LeanCopilot uses, requires CUDA 12):
https://github.com/lean-dojo/LeanCopilot/assets/118628756/31f19091-a58d-4d97-afc0-cff37490afce

I looked into some early versions of Lean Copilot, and found that the checkout flags in lakefile.lean were set to the latest commits of each dependency, which works fine at the time of a particular release, but will soon introduce problems as new Lean versions are published. This is because even when an older version of Lean Copilot is checked out, the dependencies will be updated to the latest versions when running lake update, causing a mismatch between Lean versions being used by Lean Copilot and by these packages Lean Copilot depends on. This is indeed related to the problems you reported.
We have fixed this in the recent releases, to always checkout specific corresponding commits in any release of Lean Copilot. In this way, the Lean versions should stay consistent within a release. I just tried Lean Copilot's latest stable release v1.5.0, which is to be used with Lean's latest stable release v4.10.0, and found it work fine as expected on x86_4 Linux machines with or without GPU, as well as Arm64 Linux/Mac machines.

And thank you @realharryhero for the interest and kind words! Indeed we find it interesting and potentially useful to support simple plugging-in for various models, and to enable users to try and compare them natively in Lean with the support of Lean Copilot. We are currently working on a PR #97 that adds several external models and supports this point of usage. We aim to have this PR merged, and thus have this additional feature supported, in the next stable release.

Feel free to let me know if any further difficulties are encountered, or if there is any other feature you would like us to add to Lean Copilot. Thanks for the insightful discussion! I will ping here once PR #97 is merged and the new feature is supported.


feat: Prepared some scripts to handle loaded ARM templates

Description
Porting changes from https://github.com/Azure/ResourceModules/pull/4391 to prepare later migration of StorageAccount module that requires a nested ARM template

Pipeline

Closing in favor of upcoming storage account PR that needs this change


Avoid extra buffer indirection during decoding

I noticed that the profiler recorded a lot of time in the copyto! code, so I tried refactoring that. To me it seems to be equivalent behavior, let's see what CI says. The memory gain is actually not much, but the time gained is about half.
# before
@time JpegTurbo.jpeg_decode("some_25MP.jpg")
0.860027 seconds (32 allocations: 169.126 MiB, 0.46% gc time)

# after
0.419224 seconds (30 allocations: 169.108 MiB, 1.75% gc time)

Thanks for fixing this! I knew there's room for improvement here but I failed to figure out how.


Bash - Reinstall needed ?

Just a quick question, is a bash reinstall is needed after each new preview build received ? Or nothing is required except an apt-get update/upgrade ?
Thank you in advance.

a reinstall is needed every time. however if you want that done automatically, run this command in CMD (not bash)
lxrun /install /y

Thank you very much.

This is not correct. A reinstall is not required when a preview build is released. The user mode Ubuntu bits are not updated with the flights and we support "build to build" Windows updates.

Then if I'm not subscribing preview build, there is no way to upgrade Bash on Ubuntu on Windows?

I believe what @benhillis is saying, is that the system bits will update on their own with Windows, and the Linux bits will update with apt-get

Correct.


Support MPICH and OpenMPI variants.  Enable python bindings.

Checklist

[x] Used a fork of the feedstock to propose changes
[x] Bumped the build number (if the version is unchanged)
[x] Reset the build number to 0 (if the version changed)
[x] Re-rendered with the latest conda-smithy (Use the phrase @conda-forge-admin, please rerender in a comment in this PR for automated rerendering)
[x] Ensured the license file is being packaged.

The libsharp package is used in a couple of other packages:

https://github.com/healpy/pysm
https://github.com/hpc4cmb/toast

And these require the python bindings and MPI support.  This PR enables the 3 standard MPI variants ("nompi", "mpi_mpich", and "mpi_openmpi") used by hdf5 for example.  The python bindings are now also installed.
One aspect of this work that I am unsure of is the build number.  In other packages I used the build number to prioritize the "nompi" variant by setting it to the real build number + 100.  However in this package the build number is already at 1000...

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

Hmmm, the linux package (all 3 variants) builds fine for me within the conda anvil docker container.  The bash error in all these failures looks like an infrastructure issue perhaps?

@conda-forge-admin, please rerender

Ok, looks like a systematic problem on OS X with the cython bindings.  I won't be able to test locally on OS X for a couple days.  Will try and debug it at that time.

Hello @beckermr, any feedback on this PR is welcome.  In particular the build number question in the first comment.  Thanks for your time!

@conda-forge-admin, please rerender

Hi! This is the friendly automated conda-forge-webservice.
I tried to re-render for you, but it looks like there was nothing to do.

Pinging @beckermr again on this- can we merge?  Happy to change anything you think is needed.  Feel free to add me as a maintainer if that is helpful ;-)

Happy New Year @beckermr :-)  Any thoughts on this PR?  I know you are very busy with other conda topics (thanks for holding the line on OpenMP and trying to make the case to get the libgomp patch upstreamed).

Also, if you want to add your name to the recipe maintainers list @tskisner, please do so! Then you can self-merge when I forget PRs for a month.

LGTM!


yarn create grandstack-app myNewApp fails

This process fails, so does "npx create-grandstack-app myNewApp".
Windows 10. Yarn v1.22.4.
error An unexpected error occurred<EMAIL_ADDRESS>ESOCKETTIMEDOUT".',
failed: true,
timedOut: false,
isCanceled: false,
killed: false,
context: {} }

This looks like a network connection issue when pulling down one of the dependencies. Do you receive the same error multiple times when retrying the command?


How did you choose features for NN?

Hello CuteChibiko,
It seems that you did not use all the features you constructed for NN, so I'm curious about how you choose features for NN.
thx.

Hi extremin,
I made about 600 features, then used step-wise method(brute-force attack) to choose features not only for NN but also LGBM model.

Just like forward feature selection？600features, this is a lot of work！

Yes, mainly forward(adding) feature selection, sometimes deleted one by one.
Most of 600 features are variation, like
'ip',
'app',
'device',
'os',
'channel',
'ip_app',
'ip_device',
'ip_os',
'ip_channel',
'app_device',
・・・
So it's a lot of work for my PC, not for me. :)

Oh I mean that selecting dozens of features from hundreds of features is a lot of work.

You are right, this feature selection was a lot of work and very time consuming.
I used more than one month X 24 hours.

Congrats for your LB result!

how much RAM memory needed to run the model2? 64GRAM eonough?


undefined always

No matter how many times I describe shout as a function, the window always says shout is undefined when I enter "learn test"

/needsMoreInfo
Please update initial post with code examples, steps to reproduce, and screenshots/recordings to show your problem. At this moment it's hard to understand what you are trying to achieve.

Thanks for creating this issue! We figured it's missing some basic information or in some other way doesn't follow our issue reporting guidelines. Please take the time to review these and update the issue.
Happy Coding!


[EBPF] GPU-monitoring: added nvml lib path config knob

What does this PR do?
allows to provide a custom path to libnvml-so native lib path
Motivation
can simplify the deployment in containerized environments and allow side-by-side libnvml-so libs
Describe how to test/QA your changes
Possible Drawbacks / Trade-offs
Additional Notes
Jira ticket

/merge


Questions about residual image

Hi, thank you for your great works!! :+1: :)
Could you tell me about 'residual' param?
Can I set residual to FALSE?

Thanks for your interest and sorry for the later reply.  The residual parameter refers to whether to use the residual image during training. It's set to True for multi-scan task and False for single-scan task.

If I set residual False, weight has an error! (different kernel size)
(Btw, I success to infer your code when setting residual 'True'.)
Can you update another pre-trained weights for single scan task?


Log rotation

Can you add log rotation?
Now a log file can grow very large, maybe you can add a option in the config to create a log file for very day?
and delete logs older than 10 days?

We don't plan to change logging at this time, but it's something to consider. What platform are you using to run the proxy service?

We have running it in windows and linux. And the logfiles will grow and searching to problems is much easier when the logs are split by day.

Hi @maartenpols-united, I'm the former @adobeDan.  Having retired in early March, I've now forked the Adobe open-source tools @adorton-adobe and I had written.  You can get the first release of my adlu-proxy from my ClickOneTwo repo.  It can be configured to do log rotation based on log size, if that works for you.  I have also opened clickonetwo/adobe-desktop-licensing-utilities#10 for doing date-based log rotation, please feel free to annotate that issue with any other requirements you have.


Bandwidth usage

hi,
a few days ago i got some new proxies and wanted to try the gradient bot.
so i copied 50 ips in the bot and started.
after a few seconds the vps was on its limits. its a contabo 4core 6GB.
as i seen download activities like never before, i looked at the proxy homepage and i smoked 400Mb wihin a few minutes.
i realized, that the node software is downloaded for every instance.
i guess, you had to do it like that for some id reason,otherwise im sure, you wouldnt waste that much proxy bandwidth.
can u tell me, how much bandwidth every ip would use until its running and how much do they need in operation?
the proxy provider didnt realized, how fast i use my volume. i was able to use 1,92 Gb of proxies with a 1Gb plan xD

In reality, this script launches a browser process in Docker and loads the extension.
Each browser instance requires approximately 400MB of memory, making it difficult for Contabo servers to handle too many nodes.


Put an anchor option

I think that it will be nice to have an anchor option, accepting css  selector (e.g. #myApp), that will animati when the anchor element reach the offset. This will allow to do "group animations" in sequence

I think this is a bad pattern for a React library. In generel, it is better to prevent direct access of a DOM node (via selectors i.e). in React. This can break React's internal. We are generally accessing nodes through refs,  you can check it in here: https://beta.reactjs.org/learn/manipulating-the-dom-with-refs


Project maintenance

Hi,
I'm CTO of Jazzy Innovations company, based in Poland. Our company is using plato in several JS projects to track code quality over time. It's a great tool but we find more and more cases when it would be lovely to have plato extended here and there.
My question to @jsoverson (I blamed you on "Needs maintainer" entry in README :p) is: what are your expectations on maintenance of this library? I was thinking about making this project go forward with our team, we want to keep it open-source of course.
My question to community is: would you like to have this lib maintained? What are you missing or what you'd like to have changed?
Maybe there's already another tool to use? Or another fork of this lib which is growing and have potential?

I'm also interested in maintaining this. Our needs are really to do with support for larger projects, such as:

aggregate values through a directory structure for project overview
search for files
export different formats (e.g. HTML as now, JSON) so we can use different reporters and open up/separate the visualisation side of things
pass history to CLI, so we don't have to keep history either in git or build server or whatever
support for JSX out of the box (sorry it feels dirty, but it's so damn common these days)
support for custom evaluators (essentially a plugin system)
constraints for CI integration (fail if a file is particularly unmaintainable for example, or if overall project maintainability reaches a threshold)

I would definitely contribute if someone was actively maintaining

If you look under the forks of plato, you will see that there is a more recent, maintained version of the tool: https://github.com/the-simian/es6-plato. I haven't tried it out yet, but it is worth looking into it.

There are a few things I'd like to see:

The ability for it to see () => {} functions. As it stands right now, unless the word function is used, I can't see what exactly the cog icons are pointing to.

@sradevski - Respectfully, I took a look at the repo you mentioned (as well as the one it's forked from) and neither seem to have a release later than 2014.

@eshinn Yeah, it seems that is true for the releases, but there are recent commits and I just judged based on that. I will try it out when I find the time and let you know if it is any good.

@es-analysis could we get some more contributors added to the organization? I'd be willing to contribute, along with a few individuals above.

I tried the fork https://github.com/the-simian/es6-plato and it works great! The last release on npm was 4 months ago, and there are no open issues with the project. I ran it on 2 of my React projects and it worked without any problem. I suggest you to look into it.

@zephraph this project needs a new maintainer, not just a few contributors. I can offer no time at all.
If someone wants to be the new maintainer, then submit a PR that incorporates any of the existing pull requests + new changes required and I'll add them to the group and give them publish access on npm.

@jsoverson it might make sense to ping @the-simian to see if will merge this changes into the main project?

@jsoverson it might make sense to ping @the-simian to see if will merge this changes into the main project?

I'll make a PR, not sure what the merge would look like at this point, I refactored a lot of stuff

Sorry, I've wanted to do this for some time, but I've been really overwhelmed. I've gotten my fork stable, and working, and I've fixed issues at they have come up, but I've been having a hard time to find time to make a possibly difficult PR (there could be a lot of merge conflicts at this point).
I appreciate the ping, I'll do it when I can, but I've had a challenge finding time to re-integrate to this one. I think that's a big job

So is this project maintained? or there are other forks actively maintained and updated.
@the-simian are you actively maintaining this project.

@jjpatel361 I am maintaining es6-plato right now, and I keep it up to date. I've not merged it into this projected mainly because I've heavily refactored the code to be simpler and I've added a lot of important things over time, its diverged enough
At some point I'd like to merge it all back in. I think since I forked some time ago, this project did add back in some es6-compatibility and stuff, but I havent taken the time to analyze all the updates since I forked.
SO the short answer is : I am actively maintaining my branch. The project lead here did make me a contributor and I've unfortunately not taken advantage of that yet and pulled the efforts i've made back into this 'more popular' repo.

Just a note... I have updated the underlying complexity reporting module, typhonjs-escomplex to 0.1.0. I've moved to Babel parser and all of the latest edge JS code should work. Bonus is that Typescript is now supported as well. I'll be submitting a PR soon to plato with the updates necessary. Some of the report structure has changed, so I'll be glad to do the update. @jsoverson Plato lives! ;P

@jsoverson
thanks for great tool plato
i wonder how is going on maintenance plan!
still do you looking for active maintainer?
i try to use plato in my es6 project however i failed because of some bugs.
of course, I solved the problem by forking, updating typhonjs-escomplex to 0.1.0, customizing the running eslint, and so on.
so i wanna send PR to fix some issue, but I do not know whether to choose your repo or @the-simian's.
i prefer to yours that is original and popular, but no more maintain.
if you want to keep going your project and still look for maintainer
I’m not sure if I have enough time to do this, but I will try your suggestion.

If someone wants to be the new maintainer, then submit a PR that incorporates any of the existing pull requests + new changes required and I'll add them to the group and give them publish access on npm.

if you don’t care about your project no more, how about you formally inform in README your followers to send the-simian’s repo after archiving yours?
wish others do not waste time worrying like me.

@the-simian if you're still actively maintaining es6-plato then you might as well just take this over and replace it with whatever state your fork is in.

@jsoverson
check it out #218

@jsoverson I saw this message, and I won't be able to look at this until after the 4th of July. I think that's a pretty good idea, tbh. Let me think about it. I know a project of this size, with all the Babylon ecosystem changes and things requires some close attention, tbh. I want to make sure I can stay atop of things if I do this.

@kyungilpark thanks for the PR but what this project needs is a full maintainer.
@the-simian seems best suited for it.

@jsoverson
Yes, I think so.
But he has not yet given a clear answer.
And es6-plato also has some issue for my project.
It's up to you. So I will not bother you anymore. 😭

plato is a great tool. Thank you.
We have developed a tool that has fewer features than plato, but can perform static analysis of JavaScript.
You can use it if you like.
https://github.com/ytetsuro/sabik


fix: Set kubernetes provider pin to ~> 1.11

PR o'clock
Description
This allowed me to workaround the problem mentioned in https://github.com/terraform-aws-modules/terraform-aws-eks/issues/911.
The upstream kubernetes provider had a major release (> 2.0.0) and I think it may contain a bug related to the aws_auth config map data refresh. I'm not 100% sure, but I was still encountering the problem mentioned in that issue until I overwrote the versions.tf in this repo with my change.
NOTE: I'm using Terraform 0.14.4 and Terragrunt 0.27.2 if that helps
Checklist

[ ] CI tests are passing
[X] README.md has been updated after any changes to variables and outputs. See https://github.com/terraform-aws-modules/terraform-aws-eks/#doc-generation

Closing, since you can write your required version to match the kubernetes version you need. The v2 add lot of improvement.


Information log statements flipping colors on OSX terminal

On OSX, the first time an Information level message is logged on the terminal, the foreground color is grey and background is white. Subsequent statements colors are switched with foreground color being white and background being black.
The logging should preserve the color schema of the terminal window between logging statements

@BrennanConroy is this still an issue?

Shouldn't be, we use the ascii codes for color now, so it should be a lot more consistent.


MudDatePicker and MudSelect not showing when clicked in .NET 8 blazor webassembly app

Bug type
Component
Component name
MudSelect and MudDatePicker
What happened?
Above components are not showing in my blazor webassembly project in visual studio
Expected behavior
components to work
Reproduction link
https://github.com/UdaraSandesha/MudBlazorTest
Reproduction steps

Create new blazor webassembly project in visual studio
Try to use above components
...

Relevant log output
No response
Version (bug)
7.0
Version (working)
No response
What browsers are you seeing the problem on?
Chrome
On which operating systems are you experiencing the issue?
Windows
Pull Request

[ ] I would like to do a Pull Request

Code of Conduct

[X] I agree to follow this project's Code of Conduct

Hi.
I for example don't see that you added this
<MudThemeProvider/>
<MudPopoverProvider/>
<MudDialogProvider/>
<MudSnackbarProvider/>

to MainLayout.razor in your project.
you also missing:
@using MudBlazor

in the: _Imports.razor.
Everything is explicitly and clearly written in the getting started: https://mudblazor.com/getting-started/installation#manual-install-register-services
<MudDatePicker Label="Test Date" @bind-Value="testDate" Required="true" />

there is no such API as @bind-Value in the MudDatePicker, it's @bind-Date.
When I fixed all this things in your project, everything seems to work.

Thank you I have actually use them in my real project but have missed

Thank you I have actually use them in my real project but have missed


Update README

I forgot to update README.md in https://github.com/absinthe-graphql/absinthe_relay/pull/181.
Also let's sync README files with other projects, e.g. https://github.com/absinthe-graphql/absinthe

Thank you!


A basic GUI

Basically we will have to manually run cric.py to see the gui. Some issues are yet to be addressed and solved , it is a basic structure.

@AmanDeep98, you have added unnecessary files, remove it, and remove blank lines and use space not Tab after this make a gif of running program and send PR,  post video in comment.