This PR adds an example of `parallel-agents` that runs multiple
instances of Magentic-One in parallel, with support for early
termination and final answer aggregation.

---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

Rename the `ChatMessage` and `AgentEvent` base classes to `BaseChatMessage` and `BaseAgentEvent`. 

Bring back the `ChatMessage` and `AgentEvent` as union of built-in concrete types to avoid breaking existing applications that depends on Pydantic serialization. 

Why?

Many existing code uses containers like this:

```python
class AppMessage(BaseModel):
   name: str
   message: ChatMessage 

# Serialization is this:
m = AppMessage(...)
m.model_dump_json()

# Fields like HandoffMessage.target will be lost because it is now treated as a base class without content or target fields.
```

The assumption on `ChatMessage` or `AgentEvent` to be a union of concrete types could be in many existing code bases. So this PR brings back the union types, while keep method type hints such as those on `on_messages` to use the `BaseChatMessage` and `BaseAgentEvent` base classes for flexibility.

1. Add host network support in Docker and remove unused requirements
from argument check.
2. Use Pandas to simplify summary statistic calculations. 
3. Add running time to summary statistics

```
Using tabulation method defined in '/home/ekzhu/autogen/python/packages/agbench/benchmarks/HumanEval/Scripts/custom_tabulate.py'

    Task Id       Trial 0 Success      Trial 0 Time
--  ------------  -----------------  --------------
 0  HumanEval_0   True                            3
 1  HumanEval_1   False                          15
 2  HumanEval_2   True                            2
 3  HumanEval_3   True                           11
 4  HumanEval_4   True                            4
 5  HumanEval_5   True                            2
 6  HumanEval_6   False                          18
 7  HumanEval_7   True                            2
 8  HumanEval_8   True                            2
 9  HumanEval_9   True                           12
10  HumanEval_10  False                          11
11  HumanEval_11  True                            2
12  HumanEval_12  True                            3
13  HumanEval_13  True                            1
14  HumanEval_14  True                            4
15  HumanEval_15  True                            1
16  HumanEval_16  True                            2
17  HumanEval_17  False                          76
18  HumanEval_18  True                            4
19  HumanEval_19  True                            3
20  HumanEval_20  True                            5
21  HumanEval_21  True                            3
22  HumanEval_22  True                            1
23  HumanEval_23  True                            2
24  HumanEval_24                                nan

Summary Statistics

           Successes    Failures    Missing    Total    Average Success Rate    Average Time    Total Time
-------  -----------  ----------  ---------  -------  ----------------------  --------------  ------------
Trial 0           20           4          1       25                     0.8           7.875           189

CAUTION: 'autogenbench tabulate' is in early preview and is not thoroughly tested.
Please do not cite values from these calculations in academic work without first inspecting and verifying the results in the run logs yourself.

```

Now the default tabulate output looks like this

---------

Co-authored-by: Ryan Sweet <rysweet@microsoft.com>

- Updated HumanEval template to use AgentChat
- Update templates to use config.yaml for model and other configuration
- Read environment from ENV.yaml (ENV.json still supported but
deprecated)
- Temporarily removed WebArena and AssistantBench. Neither had viable
Templates after `autogen_magentic_one` was removed. Templates need to be
update to AgentChat (in a future PR, but this PR is getting big enough
already)

This PR removes the older `autogen_magentic_one` package, and directs
people to use the new AgentChat implementation.

Hopefully this eases confusion.

---------

Co-authored-by: Jack Gerrits <jack@jackgerrits.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

* Replace create_completion_client_from_env with component config

* json load

* Move model context out of components

* move models out of components

* rename docs file

* Update agent doc

* Remove outdated doc

* Update references

* Update readme

* Update readme

* fix doc on distributed runtime

* Fix references

* Update references

* Fix import paths in user guide notebooks for code executor components

* Move grpc runtimes to ext, flatten application

* rename to grpc

* fmt

1. convert dataclass types to pydantic basemodel 
2. add save_state and load_state for ChatAgent
3. state types for AgentChat
---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

* Flatten core base and components

* remove extra files

* dont export from deprecated locations

* format

* fmt

* migrate models

* Update python/packages/autogen-agentchat/src/autogen_agentchat/agents/_tool_use_assistant_agent.py

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

* refactor missing imports

* ignore type check errors

* Update python/packages/autogen-ext/src/autogen_ext/models/_openai/_model_info.py

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

* update packages index page

---------

Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

* Move from tomllib to tomli

* added example code for magentic-one + code comments

* adding benchmarks temporarily

* add license for datasets

* revert changes to magentic-one

* change license location

---------

Co-authored-by: Ryan Sweet <rysweet@microsoft.com>