# Description

I added a very rough outline of my ideation behind
`prepare_for_training` with the new `FeedbackDataset`. As discussed
there are 3 complexities:

- How to resolve annotator alignment?
- How to resolve optional fields, which have not been filled out? e.g.,
"Please provide a correction for prompt 1?".
- How handle potential concatenation of fields? 

To make it modular I created a step-wise approach.

1. `Pydantic` Models that map and verify data fields, like so. By doing
this we keep the flexibility to allow for other tasks like
TextClassification and this ensures we can directly use `datasets.field`
and `dataset.questions` for defining training. We could also use the
`name` values from the fields/questions, but this might be more error
prone.
2. `get_relevant_data_for_training()` in `List[dict]` format with all
relevant fields from the Pydantic model. **annotator alignment issue**.
For now I opted for choosing the first non-zero value.
3. Forward the `List[dict]` to a similar flow we previously had.
4. Also add `dataset.unify_responses(question, Enum(strategy))`-method
5. Added `*QuestionUnifcation` to schemas to hold logic surrounding
unifying multiplier responses
6. Added `client.feedback.training`
7. Added`TrainingDataFor*` to hold logic surrounding
`prepare_for_training`-methods per task
8. Added inheritance for ArgillaTrainer

```python 
import argilla as rg
from argilla import (
    FeedbackRecord,
    LabelQuestion,
    LabelQuestionUnification,
    MultiLabelQuestion,
    TrainingDataForTextClassification,
    ArgillaTrainer
)

dataset = rg.FeedbackDataset(
    guidelines="Add some guidelines for the annotation team here.",
    fields=[
        rg.TextField(name="text", title="Human prompt"),
    ],
    questions =[
        LabelQuestion(
            name="relevant",
            title="Is the response relevant for the given prompt?",
            labels=["yes","no"],
            required=True,
            visible_labels=None
        ),
        MultiLabelQuestion(
            name="content_class",
            title="Does the response include any of the following?",
            description="Select all that apply",
            labels={"hate": "Hate Speech" , "sexual": "Sexual content", "violent": "Violent content", "pii": "Personal information", "untruthful": "Untruthful info", "not_english": "Not English", "inappropriate": "Inappropriate content"},
            required=False,
            visible_labels=4
        ),
    ]
)
dataset.add_records(
    records=[
        FeedbackRecord(
            fields={"text": "What is your favorite color?"},
            responses=[{"values": {"relevant": {"value": "yes"}, "content_class": {"value": ["hate"]}}}]
        ),
        FeedbackRecord(
            fields={"text": "What do you think about the new iPhone?"},
            responses=[{"values": {"relevant": {"value": "no"}, "content_class": {"value": ["hate"]}}}]
        ),
        FeedbackRecord(
            fields={"text": "What is your feeling about the technology?"},
            responses=[{"values": {"relevant": {"value": "yes"}, "content_class": {"value": ["sexual"]}}},
                       {"values": {"relevant": {"value": "no"}, "content_class": {"value": ["hate", "sexual"]}}},
                       {"values": {"relevant": {"value": "yes"}, "content_class": {"value": ["hate", "sexual"]}}}]
        ),
        FeedbackRecord(
            fields={"text": "Jesus Christ!"},
            responses=[{"values": {"relevant": {"value": "no"}, "content_class": {"value": ["sexual"]}}},
                       {"values": {"relevant": {"value": "no"}, "content_class": {"value": ["hate"]}}}]
        )

    ]
)

# print(dataset.question_by_name("relevant").__all_labels__)

label = LabelQuestionUnification(question=dataset.question_by_name("relevant"), strategy="majority")
training_data = TrainingDataForTextClassification(text=dataset.field_by_name("text"), label=label)

for framework in ["spacy", "transformers", "openai", "spark-nlp"]:
    formatted_data = dataset.prepare_for_training(framework, training_data, fetch_records=False, train_size=0.8)
    print(formatted_data)

trainer = ArgillaTrainer(
    dataset=dataset,
    training_task_mapping=training_task_mapping,
    framework="setfit",
    fetch_records=False
)
trainer.train("test")
```

Closes #2954
Closes #3184
Closes #3152 

**Type of change**

- [X] New feature (non-breaking change which adds functionality)
- [X] Improvement (change adding some improvement to an existing
functionality)

**How Has This Been Tested**

- [ ] Test A
- [ ] Test B

**Checklist**

- [ ] I have merged the original branch into my forked branch
- [ ] I added relevant documentation
- [ ] follows the style guidelines of this project
- [ ] I did a self-review of my code
- [ ] I made corresponding changes to the documentation
- [ ] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have added relevant notes to the CHANGELOG.md file (See
https://keepachangelog.com/

)

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Alvaro Bartolome <alvaro@argilla.io>