mteb/SWEPolyBenchRR huggingface dataset:
Renaming a file is not reflected in the editor when renaming twice to same name different case
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- VS Code Version: 1.71.2
- OS Version: macOS Monterey 12.5.1 (21G83)

Steps to Reproduce:

1. create file named `Test.go`
2. rename the file to `test.go`
3. check the title of the file's editor tab
<img width="199" alt="image" src="https://user-images.githubusercontent.com/12084604/192210829-f6b0d8a3-fc61-4215-80e0-beb5296e39ee.png">

Is still `Test.go`, not the new name `test.go`. until reopen the file.
But `test.go` rename to `Test.go` is ok.
Also `Foo.go` reanme to `test.go` is ok.
Just `Test.go` rename to `test.go` is not ok.
Notebook controller always defaulting to Pyolite with the new MRU kernel picker
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Does this issue occur when all extensions are disabled?: Yes/No

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- VS Code Version:  Latest Insiders 1.75
- OS Version:  Mac

Steps to Reproduce:

1.  Install VSCode insiders
2.  Install Julia , pyolite, Jupyter pre-release & python pre-release extension
3. Ensure MRU is used as the kernel picker
4. Create a new workspace folder and a new ipynb file with a name such as `helo_world1234.ipynb`
5. The kernel that is selected is `pyolite`
6. Create a new cell and run the cell and there is no prompt to select a kernel (proving the fact that pyolite is somehow selected by VS Code as the default kernel)

Expected experience - as a user I would be required to select a kernel
DOM order is not guaranteed when using keys
If you add a key to an each block, when that updates, the DOM order of the component is no longer guaranteed - maybe it's moving those updates to the end.

```
{{#each ones as one @text}}
  <div>{{one.text}}</div>
{{/each}}

{{#each twos as two @text}}
  <div>{{two.text}}</div>
{{/each}}

<script>
  export default {
    data()
      return {
	ones: [{ text: '1' }],
      	twos: [{ text: '2' }],
       };
    },
    oncreate() {
      this.set({ ones: [{ text: '11' }] });
    }
  }
</script>
```
If you run this, you'll see that the "ones" are placed after the "twos" once the component is created.

REPL: https://svelte.technology/repl?version=1.60.0&gist=e4d16c0723f25a51525fac0b8afd02d8
Views location is not getting updated when switching profiles 
- In my default profile with gitlens installed, move 3 gitlens views to a new view container (in secondary side bar)
- Create an empty profile
- Switch back to default profile
- 🐛 gitlens view are back in SCM instead of in their new view container

CC @alexr00
OutputParsers currently allows model to hallucinate the output of an action
### System Info

The MRKL and chat output parsers currently will allow an LLM response to generate a valid action, as well as hallucinate a "final answer" based on that response.

[Logic](https://github.com/hwchase17/langchain/blob/master/langchain/agents/chat/output_parser.py#L15)

This is because the parser is returning an AgentFinish object immediately if `FINAL_ANSWER_ACTION` is in the text, rather than checking if the text also includes a valid action. I had this appear when using the Python agent, where the LLM returned a code block as the action, but simultaneously hallucinated the output and a final answer in one response. (In this case, it was quite obvious because the code block referred to a database which does not exist)

I'm not sure if there are any situations where it is desired that a response should output an action as well as an answer?

If this is not desired behaviour, it can be easily fixable by raising an exception if a response includes both a valid action, and "final answer" rather than returning immedately from either condition.

### Who can help?

@hwchase17 @agola11 

### Information

- [ ] The official example notebooks/scripts
- [ ] My own modified scripts

### Related Components

- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

````py
from langchain.agents.chat.output_parser import ChatOutputParser

parser = ChatOutputParser()

valid_action = """Action:
```
{
"action": "Python REPL",
"action_input": "print(\'Hello world!\')"
}
```
final_answer = """Final Answer: Goodbye world!"""

print(parser.parse(valid_action))  # outputs an AgentFinish
print(parser.parse(final_answer))  # outputs an AgentAction
print(parser.parse(valid_action + final_answer))  # outputs an AgentFinish, should probably raise an Exception
````

### Expected behavior

An exception should likely be raised if an LLM returns a response that both includes a final answer, and a parse-able action, rather than skipping the action and returning the final answer, since it probably hallucinated an output/observation from the action.
Some coding problems
- [x] I have searched the [issues](https://github.com/apache/dubbo/issues) of this repository and believe that this is not a duplicate.
- [x] I have checked the [FAQ](https://github.com/apache/dubbo/blob/master/FAQ.md) of this repository and believe that this is not a duplicate.

### Environment

* Dubbo version: 2.7.3
* Operating System version: Mac
* Java version: 1.8

### Steps to reproduce this issue

1.  `org/apache/dubbo/registry/support/ProviderConsumerRegTable.java:65` Type 'ProviderInvokerWrapper<>' does not define hashCode(), but is used in a hashing data-structure.
2. Class overrides equals but not hashCode.
* org.apache.dubbo.config.AbstractConfig
* org.apache.dubbo.registry.support.ProviderInvokerWrapper
* org.apache.dubbo.remoting.buffer.AbstractChannelBuffer

Pls. provide [GitHub address] to reproduce this issue.

### Expected Result

Whether the above suggestions can be fixed or have other considerations

### Actual Result

Is it considered a bug? Will it fix?
Instance registry mode can't use mesh route
- [x] I have searched the [issues](https://github.com/apache/dubbo/issues) of this repository and believe that this is not a duplicate.
- [x] I have checked the [FAQ](https://github.com/apache/dubbo/blob/master/FAQ.md) of this repository and believe that this is not a duplicate.

### Environment

* Dubbo version: 3.0.3

### Steps to reproduce this issue

1. 启动 provider `-Ddubbo.application.register-mode=instance`
2. 启动 consumer `dubbo.application.service-discovery.migration=FORCE_APPLICATION`
3. 使用 `dubbo admin` 创建 mesh 路由
4. `org.apache.dubbo.registry.client.InstanceAddressURL#getParameters` 不能获取真正的 url 参数，导致路由规则匹配失败
![image](https://user-images.githubusercontent.com/43994656/134940925-96081a6c-500c-4b71-89cc-bbd22d0be296.png)
5. 原因在于 `getProtocolServiceKey()` 方法是从`RpcContext`中获取，此时获取的`protocolServiceKey` 为 null，导致获取的为 instance 参数，实际需要获取对应服务的参数。
```
    public Map<String, String> getParameters() {
        String protocolServiceKey = getProtocolServiceKey();
        if (StringUtils.isEmpty(protocolServiceKey)) {
            return getInstance().getAllParams();
        }
        return getServiceParameters(protocolServiceKey);
    }
```
```
    public String getProtocolServiceKey() {
        return RpcContext.getServiceContext().getProtocolServiceKey();
    }
```
topic length between 128 and 255
1. describe : Why are these Two checks different? Whether there are errors when the length of the topic is between 128 and 255

2. What did you see instead?
2.1. send message
org.apache.rocketmq.client.Validators#checkTopic
` if (topic.length() > CHARACTER_MAX_LENGTH) { //255
            throw new MQClientException("The specified topic is longer than topic max length 255.", null);
        }`
2.2. store message
org.apache.rocketmq.store.DefaultMessageStore#putMessage
org.apache.rocketmq.broker.processor.SendMessageProcessor#sendBatchMessage
org.apache.rocketmq.broker.processor.AbstractSendMessageProcessor#msgContentCheck
`if (msg.getTopic().length() > Byte.MAX_VALUE) { //127
            log.warn("putMessage message topic length too long " + msg.getTopic().length());
            return new PutMessageResult(PutMessageStatus.MESSAGE_ILLEGAL, null);
        }`
3. What did you expect to see?
org.apache.rocketmq.store.DefaultMessageStore#putMessage
org.apache.rocketmq.broker.processor.SendMessageProcessor#sendBatchMessage
org.apache.rocketmq.broker.processor.AbstractSendMessageProcessor#msgContentCheck
`if (msg.getTopic().length() > 255) { 
            log.warn("putMessage message topic length too long " + msg.getTopic().length());
            return new PutMessageResult(PutMessageStatus.MESSAGE_ILLEGAL, null);
        }`
Markdown footnotes formatted incorrectly with long lines
In markdown, for footnotes that have more characters than the column width, prettier breaks the footnote reference apart into a new line. This makes the footnote not generate properly using kramdown, as the new line after the reference block and colon makes the following text not included in the footnote.

Unfortunately, I don't have a fix contribution for this, but hope you can think about how to correct this behavior. 

This is sort of related to #3787, but this doesn't relate to blocks or codes, only to the too-long lines.

**Prettier 1.14.2**
[Playground link](https://prettier.io/playground/#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)
```sh
--parser markdown
```

**Input:**
```markdown
The eRegulations platform, which originated at CFPB is being used by other Federal agencies [^1] and continues to be improved based on public feedback; [^1]

[^1]: The Bureau of Alcohol, Tobacco, Firearms, and Explosives (ATF) has adopted a beta version of “eRegulations,” accessible at <a href="https://atf-eregs.18f.gov/">https://atf-eregs.18f.gov/</a>.
```

**Output:**
```markdown
The eRegulations platform, which originated at CFPB is being used by other Federal agencies [^1] and continues to be improved based on public feedback; [^1]

[^1]:

  The Bureau of Alcohol, Tobacco, Firearms, and Explosives (ATF) has adopted a beta version of “eRegulations,” accessible at <a href="https://atf-eregs.18f.gov/">https://atf-eregs.18f.gov/</a>.

```

**Expected behavior:**
**Output:**
```markdown
The eRegulations platform, which originated at CFPB is being used by other Federal agencies [^1] and continues to be improved based on public feedback; [^1]

[^1]: The Bureau of Alcohol, Tobacco, Firearms, and Explosives (ATF) has adopted a beta version of “eRegulations,” accessible at <a href="https://atf-eregs.18f.gov/">https://atf-eregs.18f.gov/</a>.

```
Not handled case when use_weighted_layer_sum and return-dict=True in WhisperForAudioClassification
@sanchit-gandhi 
I use WhisperForAudioClassification task and want to use `use_weighted_layer_sum=True`, but there is a problem when call forward, 
the encoder part can return tuple or dict if `return_dict=True` but the code for use `use_weighted_layer_sum=True` assume the return to be tuple only and this line raise error `hidden_states = torch.stack(encoder_outputs, dim=1)` if the encoder return dict, there are workaround by using `return_dict=False` but when use the model later with `pipeline` it will raise error because it assume the model to return dict not tuple. [Link to code with the problem](https://github.com/huggingface/transformers/blob/c7f076a00ee54f777b3d3322c91bc11489a47950/src/transformers/models/whisper/modeling_whisper.py#L2918C6-L2918C6)
```py
        if self.config.use_weighted_layer_sum:
            hidden_states = torch.stack(encoder_outputs, dim=1) # This line raise error when return_dict=True and use_weighted_layer_sum=True
            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
        else:
            hidden_states = encoder_outputs[0]
```
**Reproduce error**
```py
from transformers import  WhisperForAudioClassification, AutoFeatureExtractor
from datasets import load_dataset

dataset = load_dataset('seba3y/speechocean762',)
dataset = dataset['train']
sampling_rate = dataset.features["audio"].sampling_rate
dataset = dataset.remove_columns(['utt_name', 'text', 'completeness', 'fluency', 'prosodic'])


feature_extractor = AutoFeatureExtractor.from_pretrained("seba3y/whisper-tiny")
model = WhisperForAudioClassification.from_pretrained("seba3y/whisper-tiny",
                                                       use_weighted_layer_sum=True, 
                                                       return_dict=True)
# test if it work
inputs = feature_extractor(dataset['train'][3]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
predicted_class_ids = torch.argmax(logits, dim=-1).item()
predicted_label = model.config.id2label[predicted_class_ids]
print(predicted_label)
```
Slow tokenizers return overflowing tokens in reversed order
When implementing the slow tokenizer for LayoutLMv2, I spotted some weird behaviour for slow tokenizers when specifying `return_overflowing_tokens = True`. Namely, in that case, overflowing tokens are returned in reversed order, and no padding is performed, unlike fast tokenizers.

Small example:

```
from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

text = "hello my name is niels"

encoding = tokenizer(text, padding=True, max_length=6, truncation=True, return_overflowing_tokens=True)
```

When checking out the encoding, it looks as follows:

```
print(tokenizer.decode(encoding.input_ids))
# prints '[CLS] hello my name is [SEP]'

print(tokenizer.decode(encoding.overflowing_tokens))
# prints '##els ni'
```

As you can see, the overflowing tokens are returned in reversed order, and they are not padded up to the max length of 6 tokens. In contrast, `BertTokenizerFast` does everything correctly:

```
from transformers import BertTokenizerFast

tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")

text = "hello my name is niels"

encoding = tokenizer(text, padding=True, max_length=6, truncation=True, return_overflowing_tokens=True)
```
returns
```
print(tokenizer.decode(encoding.input_ids[0]))
# prints '[CLS] hello my name is [SEP]'

print(tokenizer.decode(encoding.input_ids[1]))
# prints '[CLS] niels [SEP] [PAD] [PAD]'
```
So I guess we have some work to do for slow tokenizers to work correctly.

cc @LysandreJik @SaulLu @n1t0
Support Condition and DependsOn
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# This is a Feature Proposal

## Description

Allow users to specify `condition` and `dependsOn` on functions and compile them to CloudFormation `Condition` and `DependsOn` for the Lambda function resource.
`center_crop` outputs wrong sized array if provided with odd-numbered dimensions smaller than requested crop size
### System Info

transformers 4.40.1, python 3.12

### Who can help?

@amyeroberts 

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

```
from transformers.image_transforms import center_crop
import numpy as np
small_even = np.ones((3,4,4))
small_odd = np.ones((3,3,3))
big_even = np.ones((3,10,10))
big_odd = np.ones((3,11,11))
print([center_crop(x, (8,8)).shape for x in [small_even, big_even, big_odd, small_odd]])
```

Result:
```
[(3, 8, 8), (3, 8, 8), (3, 8, 8), (3, 7, 7)]
```

### Expected behavior

All arrays should be cropped to the requested size of (8,8). However, odd-numbered dimensions that are smaller than the crop size and require padding result in an unexpected off-by-one output size.
End of line comment in template literal turns into invalid
```js
`
${// $FlowFixMe found when converting React.createClass to ES6
ExampleStory.getFragment('story')}
`
```

turns into

```js
`
${ExampleStory.getFragment('story')} // $FlowFixMe found when converting React.createClass to ES6
`;
```

which changes the meaning since the comment is now part of the text of the template literal.

https://jlongster.github.io/prettier/#%7B%22content%22%3A%22%60%5Cn%24%7B%2F%2F%20%24FlowFixMe%20found%20when%20converting%20React.createClass%20to%20ES6%5CnExampleStory.getFragment('story')%7D%5Cn%60%22%2C%22options%22%3A%7B%22printWidth%22%3A80%2C%22tabWidth%22%3A2%2C%22singleQuote%22%3Atrue%2C%22trailingComma%22%3Atrue%2C%22bracketSpacing%22%3Atrue%2C%22doc%22%3Afalse%7D%7D
DX: Validate Cron Syntax Before Deploy & Improve Error Message
Cron is weird and not human friendly.

I'd love for the framework to validate my stupid human mistakes before I have to wait for the deploy to fail with a vague error message.

![image](https://cloud.githubusercontent.com/assets/532272/23385855/9156a364-fd07-11e6-96f9-90079819a675.png)

Ideally the framework would validate the cron expression validity before `deploy` runs and returns errors quickly for faster fixing.

Additionally it would be great if we started giving better error messages. Like for example a better error message would be:

```
 An error occurred while provisioning your stack: PublishScheduledPostCronEventsRuleSchedule1
     - Parameter ScheduleExpression is not valid..

Please check your cron syntax. Do additional AWS cron information see http://docs.aws.amazon.com/lambda/latest/dg/tutorial-scheduled-events-schedule-expressions.html
```

Little things like this go a long way in terms of developer experience.
`DebertaTokenizer` always assigns token type ID 0
## Environment info

- `transformers` version: 4.16.2
- Platform: Linux-5.15.13-051513-generic-x86_64-with-glibc2.34
- Python version: 3.9.7
- PyTorch version (GPU?): 1.9.0+cu111 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no


### Who can help
@LysandreJik 

## Information

Model I am using (Bert, XLNet ...): `microsoft/deberta-large`

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

Run this code:
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-large")
print(tokenizer("Hello", "World"))
```

It outputs:
```
{'input_ids': [1, 31414, 2, 10988, 2], 'token_type_ids': [0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1]}
```

Even though I put in two sequences, all `token_type_ids` are 0.

## Expected behavior

The tokens from the second sequence should get type ID 1. `token_type_ids` should be `[0, 0, 0, 1, 1]`.
HTML Angular parser removes necessary parentheses causing early block closing
**Prettier 1.15.3**
[Playground link](https://prettier.io/playground/#N4Igxg9gdgLgprEAuEAeADgPgDpQAQF7DC6Fl4DkAJnADZzwWnksA+eMATgIZQDOtbvCREA7gAshIgBQUEMAJaK4fCnnZdeAoXBHBIAV1gjunHgE8AdPSgBzGOIC+ASkfMCjt1FQB6LCAAaEAh0RWg+ZFBTTghRAAVTBAiUbgA3CAUqQJAAIx4wAGsGAGV0bjAFO2QAM25aPjgg8RgAW1oAdXElFTKwOGKkpQVUpXNkcD4IoMqGzhg4nlsW7hq6hqCAKz4ADwAhfKKYYu4WuAAZSrhV+saQLe3iytt6AEUDCHhr9ZAyzlnx3i2AyCTjZdCcSowdqZBzIAAcAAYguCIA12jx0ONwSo4JxUlcgpw4ABHAwKIkLbhLFZIWo3IINFoKZBcAy3PhPV7vT60ta3GDcHLQqiwpAAJiCmgUtCeAGEIC1luMoNACSADA0ACqC5J0hqeIA)
```sh
--no-bracket-spacing
--parser angular
```

**Input:**
```html
<p>
    {{
        'delete'
            | translate: {what: ('entities' | translate: {count: array.length})}
    }}
</p>
```

**Output:**
```html
<p>
  {{
    "delete" | translate: {what: "entities" | translate: {count: array.length}}
  }}
</p>

```

**Expected behavior:**
The parentheses should be kept to prevent angular from parsing the two closing curly brackets as the end of the block
Filter pattern improvement for cloudwatchLog events (aws)
# This is a Feature Proposal (Improvement)

## Description

For feature proposals:
* When adding a cloudwatchLog event, as mentioned by AWS document `Metric filter terms that include characters other than alphanumeric or underscore must be placed inside double quotes ("")`, if we want to add a filter like `"Total amount" - "level=DEBUG"`, we need to write it like this:

```yaml
events:
      - cloudwatchLog:
          logGroup: "/aws/lambda/my-lambda-log-group"
          filter: "\\\"Total amount\\\" -\\\"level=DEBUG\\\""
```
It is not straightforward and it is not obvious to users. I only found out that I need to write like above when I check the [source code](https://github.com/serverless/serverless/blob/v1.24.1/lib/plugins/aws/package/compile/events/cloudWatchLog/index.js#L87).

* Do you think it necessary to modify it and let it accept format like below:
```yaml
events:
      - cloudwatchLog:
          logGroup: "/aws/lambda/my-lambda-log-group"
          filter: '"Total amount" -"level=DEBUG"'
```

* Need to consider backwards compatible
* I can try to implement the improvement if you agree to make this change

Similar or dependent issues:
* None

## Additional Data

* ***Serverless Framework Version you're using***: v1.24.1
* ***Operating System***: Mac OSX, Linux, AWS AIM
* ***Stack Trace***:
* ***Provider Error messages***:
Incorrect result for decimal division
```
presto> SELECT cast(1000 as decimal(38,8)) / cast(25 AS decimal(38,8));
   _col0
------------
 9.16269668
(1 row)
```

It happens because https://github.com/prestosql/presto/blob/master/presto-spi/src/main/java/io/prestosql/spi/type/UnscaledDecimal128Arithmetic.java#L1503 overflows and turns negative.
Print `x: () => void` instead of `x(): void` for flow types
```js
type T = { method: () => void };
```

currently outputs

```js
type T = { method(): void };
```

but in practice, most people are using the first version in their code. Since they have the same AST, prettier needs to make a call. I think it's better to use the first one all the time. This would also be consistent with types inside of function calls.

https://jlongster.github.io/prettier/#%7B%22content%22%3A%22type%20T%20%3D%20%7B%20method%3A%20()%20%3D%3E%20void%20%7D%22%2C%22options%22%3A%7B%22printWidth%22%3A80%2C%22tabWidth%22%3A2%2C%22singleQuote%22%3Afalse%2C%22trailingComma%22%3Afalse%2C%22bracketSpacing%22%3Atrue%7D%7D
Support setting RedrivePolicty for AWS::SNS::Subscription when using sns as a trigger for a function
Similarly to the option of using a FilterPolicy when defining a sns topic as the trigger of a function, it would be useful to allow to define the dead letter target (the name or the arn) so the generated code looks something like 
````
{
                  Type: 'AWS::SNS::Subscription',
                  Properties: {
                    TopicArn: topicArn,
                    Protocol: 'lambda',
                    Endpoint: endpoint,
                    FilterPolicy: event.sns.filterPolicy,
                    RedrivePolicy: {
                        deadLetterTargetArn: {"Fn::GetAtt": [event.sns.dlqName, "Arn"]}
                    }
                    Region: region,
                  },
 },
````
`PreTrainedTokenizerFast._batch_encode_plus()` got an unexpected keyword argument `'split_special_tokens'`
### System Info

Transformer version: 4.38.1
Platform: Ubuntu
Python version: 3.10.13

### Who can help?

@ArthurZucker @younesbelkada 

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

```python
from transformers import AutoTokenizer

access_token = 'YOUR_ACCESS_TOKEN'

tokenizer = AutoTokenizer.from_pretrained('meta-llama/Meta-Llama-3-8B', token=access_token)

print(tokenizer('Here is an example of bos token: 
', split_special_tokens=True)
```

### Expected behavior

Must return this:
```python
{'input_ids': [128000, 8586, 374, 459, 3187, 315, 43746, 4037, 25, 83739, 7413, 3659, 4424, 91, 29], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
```
Unstable property key print
**Prettier 3.2.5**
[Playground link](https://prettier.io/playground/#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-H4QA)
<!-- prettier-ignore -->
```sh
--parser babel
--quote-props consistent
```

**Input:**
<!-- prettier-ignore -->
```jsx
a = {
  "foo": 1,
  "b\ar": 2,
}
```

**Output:**
<!-- prettier-ignore -->
```jsx
a = {
  "foo": 1,
  "bar": 2,
};

```

**Second Output:**
<!-- prettier-ignore -->
```jsx
a = {
  foo: 1,
  bar: 2,
};

```

**Expected output:**
<!-- prettier-ignore -->
```jsx
a = {
  foo: 1,
  bar: 2,
};
```

**Why?**

<!-- short explanation of expected output -->

Should be stable
[Bug] Can not get the correct accumulation for consumer group.
### Before Creating the Bug Report

- [X] I found a bug, not just asking a question, which should be created in [GitHub Discussions](https://github.com/apache/rocketmq/discussions).

- [X] I have searched the [GitHub Issues](https://github.com/apache/rocketmq/issues) and [GitHub Discussions](https://github.com/apache/rocketmq/discussions)  of this repository and believe that this is not a duplicate.

- [X] I have confirmed that this bug belongs to the current repository, not other repositories of RocketMQ.


### Runtime platform environment

OS：MacOS

### RocketMQ version

5.1.4

### JDK Version

JDK 11

### Describe the Bug

Can not get the correct accumulation for consumer group.

### Steps to Reproduce

Simulate consumer accumulation, then query the accumulation estimation of the group.

### What Did You Expect to See?

The correct accumulation as Admin#consumerprogress output.

### What Did You See Instead?

The wrong estimation.

### Additional Context

_No response_
wrong indent (tab width) when using ternary in jsx
**Prettier 1.7.4**
[Playground link](https://prettier.io/playground/#N4Igxg9gdgLgprEAuc0DOMAEAxCFMC8mAFAJSEB8JAOlJvZgDwAmAlgG4W0M+bABmeQgSJQArgBsJ3XrID8maiCUzZPJJkH4RRGACcxcVWp4KlATThoVdE7w1KAchCUBfY4wD0bTrVIBuWhAAGhAIAAcYVnRkUABDDGR+OIk0OFCAIz04sABrOBgAZXCc1igAc2R9Q1DmCDAklLTQgCs0AA8AIWy8gsK4gFs4ABkyuEbU9LCxGHCZgCYJ5pASvTS9ZBAMuIyATwloEJW9MpgAdVZmGAALZAAWAAZQtIHWKoMptDLyiTgARTEEHgSymMB2FyutyQd1C+jirAk3wAwhABgM4psoNBxqExGkACo7NAg1yuIA)
```sh
--print-width 40
--tab-width 4
```

**Input:**
```jsx
const Foo = () => (
    <div>
        {foo === null
            ? ""
            : foo === true
              ? "Yes"
              : "No"}
    </div>
);

```

**Output:**
```jsx
const Foo = () => (
    <div>
        {foo === null
            ? ""
            : foo === true
              ? "Yes"
              : "No"}
    </div>
);

```

**Expected behavior:**
```diff
 const Foo = () => (
     <div>
         {foo === null
             ? ""
             : foo === true
-              ? "Yes"
-              : "No"}
+               ? "Yes"
+               : "No"}
     </div>
 );

```

BTW thank you all!
Support for Serverless.JSON 
# This is a Feature Proposal
JSON support as Serverless config file (Serverless.json instead of Serverless.YML)
## Description

* Using YML feels awkard , most of the usage of lambda are done with NodeJS and Node mostly uses JSON file for configuration (Webpack, Grunt , package.json etc....)
Using YML doesn't feel right
* Most examples provided by AWS for cloudformation are using JSON ( examples here 
[AWS Labs](https://github.com/awslabs)

Similar or dependent issues:

This feature was already proposed but got lost in the different release [Optionnal Serverless.JSON](https://github.com/serverless/serverless/issues/1900)
Array access breaking issue
Input:
```js
const mostExpandedasdasdfasdfasdfasdf = doc.expandedStates[doc.expandedStates.length - 1];
```

Output:
```js
const mostExpandedasdasdfasdfasdfasdf = doc.expandedStates[doc.expandedStates.length -
  1];
```

Expected Output:
```js
const mostExpandedasdasdfasdfasdfasdf = doc.expandedStates[
  doc.expandedStates.length - 1
];
```

https://jlongster.github.io/prettier/#%7B%22content%22%3A%22const%20mostExpandedasdasdfasdfasdfasdf%20%3D%20doc.expandedStates%5Bdoc.expandedStates.length%20-%201%5D%3B%22%2C%22options%22%3A%7B%22printWidth%22%3A80%2C%22tabWidth%22%3A2%2C%22singleQuote%22%3Afalse%2C%22trailingComma%22%3Afalse%2C%22bracketSpacing%22%3Atrue%7D%7D

If it's a function call, it's handled as expected:

https://jlongster.github.io/prettier/#%7B%22content%22%3A%22const%20mostExpandedasdasdfasdfasdfasdf%20%3D%20doc.expandedStates(doc.expandedStates.length%20-%201)%3B%22%2C%22options%22%3A%7B%22printWidth%22%3A80%2C%22tabWidth%22%3A2%2C%22singleQuote%22%3Afalse%2C%22trailingComma%22%3Afalse%2C%22bracketSpacing%22%3Atrue%7D%7D
Error: 
### System Info

I try this example code

```
from langchain.retrievers import ParentDocumentRetriever
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.storage import InMemoryStore

# This text splitter is used to create the parent documents
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
# This text splitter is used to create the child documents
# It should create documents smaller than the parent
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
# The vectorstore to use to index the child chunks
vectorstore = Chroma(embedding_function=OpenAIEmbeddings())
# The storage layer for the parent documents
store = InMemoryStore()

vectorstore = Chroma(collection_name="test", embedding_function=OpenAIEmbeddings())
```

# Initialize the retriever
parent_document_retriever = ParentDocumentRetriever(
    vectorstore=vectorstore,
    docstore=store,
    child_splitter=child_splitter,
    parent_splitter=parent_splitter,
)

but I encountered an error:

```
 1 # Initialize the retriever
----> 2 parent_document_retriever = ParentDocumentRetriever(
      3     vectorstore=vectorstore,
      4     docstore=store,
      5     child_splitter=child_splitter,

TypeError: MultiVectorRetriever.__init__() got an unexpected keyword argument 'child_splitter'
```

### Who can help?

_No response_

### Information

- [X] The official example notebooks/scripts
- [ ] My own modified scripts

### Related Components

- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

```
from langchain.retrievers import ParentDocumentRetriever
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.storage import InMemoryStore

# This text splitter is used to create the parent documents
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
# This text splitter is used to create the child documents
# It should create documents smaller than the parent
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
# The vectorstore to use to index the child chunks
vectorstore = Chroma(embedding_function=OpenAIEmbeddings())
# The storage layer for the parent documents
store = InMemoryStore()

vectorstore = Chroma(collection_name="test", embedding_function=OpenAIEmbeddings())
# Initialize the retriever
parent_document_retriever = ParentDocumentRetriever(
    vectorstore=vectorstore,
    docstore=store,
    child_splitter=child_splitter,
    parent_splitter=parent_splitter,
)
```

### Expected behavior

I can run.
Default values don't work in variables within ${file}
<!--
1. If you have a question and not a bug report please ask first at http://forum.serverless.com
2. Please check if an issue already exists. This bug may have already been documented
3. Check out and follow our Guidelines: https://github.com/serverless/serverless/blob/master/CONTRIBUTING.md
4. Fill out the whole template so we have a good overview on the issue
5. Do not remove any section of the template. If something is not applicable leave it empty but leave it in the Issue
6. Please follow the template, otherwise we'll have to ask you to update it
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# This is a Bug Report

## Description

After updating to serverless 1.37 our serverless.yml (almost) silently stopped setting environment to lambdas. The issue ended up being related to default values of variables when including external files.

Here is an example config:

```
service: my-service

custom:
  config: ${file(config/${opt:stage, 'development'}.yml)}

provider:
  name: aws
  stage: ${self:custom.stage}
  environment: ${self:custom.config.environment}
```

The output of `sls print -s production` is:

```
service: my-service
custom:
  config:
    environment:
      IS_PRODUCTION: 'True'
provider:
  stage: production
  name: aws
```

Additionally, serverless shows a warning: `A valid service attribute to satisfy the declaration 'self:custom.config.environment' could not be found.`

If I remove the default value for `opt:stage` and use the following config, the problem disappears:

```
sls print -s production

service: my-service
custom:
  config:
    environment:
      IS_PRODUCTION: 'True'
provider:
  stage: production
  name: aws
  environment: <<< ENVIRONMENT IS PRESENT NOW
     $ref: '$["custom"]["config"]["environment"]'
```

The same config (with the default value) works just fine with `serverless@1.36.3`.
Transformer-XL tokenizer cannot properly tokenize brackets
# 🐛 Bug

## Information
The `TransfoXLTokenizer` is not able to tokenize words with surrounding brackets correctly. I compared it with the `BertTokenizer` from `bert-base-uncased` which gives the expected result. Example text is: `"Hello (bracket)"`

Model I am using: **Transformer-XL**

Language I am using the model on: **English**

The problem arises when using:
* [x] my own modified scripts

## To reproduce

Steps to reproduce the behavior:
```python
from transformers import BertTokenizer, TransfoXLTokenizer

bert = BertTokenizer.from_pretrained('bert-base-uncased')
transfoxl = TransfoXLTokenizer.from_pretrained('transfo-xl-wt103')

def test_bracket(tokenizer):
    enc = tokenizer.encode("Hello (bracket)")
    dec = tokenizer.decode(enc)
    print(f"ORG: Hello (bracket)\nENC: {enc}\nDEC: {dec}")
```
Results:  
`test_bracket(bert)` gives the following output:
```
ORG: Hello (bracket)
ENC: [101, 7592, 1006, 21605, 1007, 102]
DEC: [CLS] hello ( bracket ) [SEP]
```

`test_bracket(transfoxl)` gives the following output:
```
ORG: Hello (bracket)
ENC: [14049, 24]
DEC: Hello <unk>
```
If the parameter `add_space_before_punct_symbol=True` is passed, then the result is:
```
ORG: Hello (bracket)
ENC: [14049, 24, 21]
DEC: Hello <unk> )
```

<!-- If you have code snippets, error messages, stack traces please provide them here as well.
     Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
     Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.-->

## Expected behavior
The `TransfoXLTokenizer` should detect the punctuation symbols, e.g. `(`, separately and thus give the same result as the `BertTokenizer` (except the special tokens of course): `hello ( bracket )`


## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: 2.11.0
- Platform: Windows-10-10.0.18362-SP0
- Python version: 3.6.10
- PyTorch version (GPU?): 1.4.0 (False)
- Tensorflow version (GPU?): 2.1.0 (False)
USE fails when in an invalid catalog when a role for that catalog is set
```
$ presto --debug --catalog test
presto> set role admin;
SET ROLE
presto> use tpch.tiny;
Query 20200208_184255_00004_a53s9 failed: Catalog does not exist: test
io.prestosql.spi.PrestoException: Catalog does not exist: test
        at io.prestosql.Session.lambda$beginTransactionId$3(Session.java:332)
        at java.util.Optional.orElseThrow(Optional.java:290)
        at io.prestosql.Session.beginTransactionId(Session.java:332)
        at io.prestosql.execution.QueryStateMachine.beginWithTicker(QueryStateMachine.java:231)
        at io.prestosql.execution.QueryStateMachine.begin(QueryStateMachine.java:198)
        at io.prestosql.dispatcher.LocalDispatchQueryFactory.createDispatchQuery(LocalDispatchQueryFactory.java:98)
        at io.prestosql.dispatcher.DispatchManager.createQueryInternal(DispatchManager.java:192)
        at io.prestosql.dispatcher.DispatchManager.lambda$createQuery$0(DispatchManager.java:146)
        at io.prestosql.$gen.Presto_null__testversion____20200208_184148_3.run(Unknown Source)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
        at java.lang.Thread.run(Thread.java:748)
```
Multiple cursors + Word wrap
The current behaviour in VSCode with `"editor.wordWrap": "on" ` is like this:
![Image](https://cloud.githubusercontent.com/assets/5332158/19222557/a85d2168-8e31-11e6-985a-e328a6c3b11c.gif)

But I would prefer an option for this
![Image](https://cloud.githubusercontent.com/assets/5332158/20053063/ffbafc3a-a4bd-11e6-9f92-80733ca6c606.gif)

Note I used the gif from [here](https://github.com/SublimeTextIssues/Core/issues/1054) which I found while looking for a solution. 
Multiple cursors + Word wrap
The current behaviour in VSCode with `"editor.wordWrap": "on" ` is like this:
![Image](https://cloud.githubusercontent.com/assets/5332158/19222557/a85d2168-8e31-11e6-985a-e328a6c3b11c.gif)

But I would prefer an option for this
![Image](https://cloud.githubusercontent.com/assets/5332158/20053063/ffbafc3a-a4bd-11e6-9f92-80733ca6c606.gif)

Note I used the gif from [here](https://github.com/SublimeTextIssues/Core/issues/1054) which I found while looking for a solution.
{{Yield}} content isn't injected in the correct place
per docs https://svelte.technology/guide#yield-tags:
The button below appears ***above*** the yielded content instead of below it
```
<div class='modal'>
  {{yield}} <!-- content is injected here -->
  <button on:click='destroy()'>close modal</button>
</div>
```
{{Yield}} content isn't injected in the correct place
per docs https://svelte.technology/guide#yield-tags:
The button below appears ***above*** the yielded content instead of below it
```
<div class='modal'>
  {{yield}} <!-- content is injected here -->
  <button on:click='destroy()'>close modal</button>
</div>
```
org.apache.rocketmq.common.stats.StatsItemSet#getAndCreateStatsItem has multi-thread problem
The code below
```
    public StatsItem getAndCreateStatsItem(final String statsKey) {
        StatsItem statsItem = this.statsItemTable.get(statsKey);
        if (null == statsItem) {
            statsItem = new StatsItem(this.statsName, statsKey, this.scheduledExecutorService, this.log);
            StatsItem prev = this.statsItemTable.put(statsKey, statsItem);

            if (null != prev) {

                // statsItem.init();
            }
        }

        return statsItem;
    }
```
has multi-thread problem. Because we can not ensure the atomicity of

```
StatsItem statsItem = this.statsItemTable.get(statsKey);
StatsItem prev = this.statsItemTable.put(statsKey, statsItem);
```
Here is the test case. The result is not always the correct one 20000.

```
public class BrokerStatsManagerTest {

    private BrokerStatsManager brokerStatsManager;
    private ThreadPoolExecutor executor;
    @Before
    public void init() {
        brokerStatsManager = new BrokerStatsManager("DefaultCluster");
        executor = new ThreadPoolExecutor(100, 200, 10, TimeUnit.SECONDS, new ArrayBlockingQueue<Runnable>(1000), new ThreadFactoryImpl("testMultiThread"));
    }

    @Test
    public void testBrokerStatsManager() throws InterruptedException {

        for(int i =0; i < 10000; i++) {
            executor.submit(new Runnable() {
                @Override
                public void run() {
                    brokerStatsManager.incTopicPutNums("topicTest", 2, 1);
                }
            });
        }
        Thread.sleep(5000);
        System.out.println(brokerStatsManager.getStatsItem(TOPIC_PUT_NUMS,"topicTest").getValue());
    }
}
```
Improve "Invoke Local"
# Problem
We've added `invoke local` with the goal of emulating Lambda as best as it can, so that developers can use it to speed up their workflow.

We tried to get an MVP of local support in ASAP, and succeeded.  However, it missed a few critical pieces:  

**1 - `invoke local` behaves differently than `invoke`**

**2 - `invoke local` does not include the environment variables available in AWS Lambda**

**3 - `invoke local` does not support our other runtimes**

# Solution

### 1

This is already being fixed by @horike37 in PR #2620.

### 2

We should create a function that collects the ENV vars before the local invocation happens.  Here are the missing ENV vars.  We have enough information to find/create most of their values locally.

`LAMBDA_TASK_ROOT`
`LAMBDA_RUNTIME_DIR`
`AWS_REGION`
`AWS_DEFAULT_REGION`
`AWS_LAMBDA_LOG_GROUP_NAME`
`AWS_LAMBDA_LOG_STREAM_NAME`
`AWS_LAMBDA_FUNCTION_NAME`
`AWS_LAMBDA_FUNCTION_MEMORY_SIZE`
`AWS_LAMBDA_FUNCTION_VERSION`
`AWS_ACCESS_KEY_ID`
`AWS_SECRET_ACCESS_KEY`
`AWS_SESSION_TOKEN`

### 3

To easily support other languages, we should use a child process whenever we do a local invocation.  We can pass the above environment variables into that child process as an option, since that's easily supported by Node.js.

Keep in mind, the function can be long-running.  Using a callback with the child_process via something like `exec` may not be recommended, since log statements should appear whenever they are logged to `stdout`.  `on('data')` [may be preferred](https://nodejs.org/api/child_process.html#child_process_child_process)

**Note:** A lot of this functionality was already written in V.0 and is a good reference: https://github.com/serverless/serverless/blob/8239e9ca030bfbfa3e8c3f3a9d90aa6af6aec83b/lib/RuntimePython27.js
Pug plugin doesn't work for the .vue files
<!--

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    you do not need to open a new issue — please add a 👍 reaction to the
    existing issue instead.

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    issue in the repo for that extension or add-on, instead of this repo.

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**Environments:**
- Prettier Version: 1.18.2
- Running Prettier via: CLI
- Runtime: node.js v8.15.0
- Operating System: MacOS

**Steps to reproduce:**
- clone https://github.com/xorik/prettier-pug-vue
- run `prettier --write *.vue`

**Expected behavior:**
Template inside the `pug.vue` should be prettified

**Actual behavior:**
template inside the `pug.vue` isn't touched by prettier


If you run `prettier --write *.pug`, you can see that the plugin itself works perfectly.
The plugin developers suggested to write here: (https://github.com/prettier/plugin-pug/issues/5)