fix: keras fit tests for segformer tf and minor refactors.
Fixes the issues as noticed in: https://github.com/huggingface/transformers/runs/7485048615?check_suite_focus=true. 

I don't have access to an instance having multiple GPUs at the moment, but I figured out the root cause of the issue. 

https://github.com/huggingface/transformers/blob/df5e4232f59e6fea08911eddd0adc965d1b59c15/tests/models/segformer/test_modeling_tf_segformer.py#L346

^ I wasn't calling the model on some sample inputs, which is why the weights retrieved from `get_weights()` were zero. That has been fixed in this PR. 

I tested it locally in isolation with the following snippet (I acknowledge that it's not super clean):

```py
from transformers import TFSegformerForImageClassification, TFSegformerForSemanticSegmentation, SegformerConfig

import tensorflow as tf

from tests.test_modeling_tf_common import floats_tensor, ids_tensor
import numpy as np

batch_size = 13
image_size = 64
num_channels = 3
num_encoder_blocks = 4
depths = [2, 2, 2, 2]
sr_ratios = [8, 4, 2, 1]
hidden_sizes = [16, 32, 64, 128]
downsampling_rates = [1, 4, 8, 16]
num_attention_heads = [1, 2, 4, 8]
is_training = True
use_labels = True
hidden_act = "gelu"
hidden_dropout_prob = 0.1
attention_probs_dropout_prob = 0.1
initializer_range = 0.02
num_labels = 3


def get_config():
    return SegformerConfig(
        image_size=image_size,
        num_channels=num_channels,
        num_encoder_blocks=num_encoder_blocks,
        depths=depths,
        hidden_sizes=hidden_sizes,
        num_attention_heads=num_attention_heads,
        hidden_act=hidden_act,
        hidden_dropout_prob=hidden_dropout_prob,
        attention_probs_dropout_prob=attention_probs_dropout_prob,
        initializer_range=initializer_range,
        num_labels=num_labels
    )

def prepare_config_and_inputs(for_semseg=True):
    pixel_values = floats_tensor([batch_size, num_channels, image_size, image_size])

    if for_semseg:
        labels = ids_tensor([batch_size, image_size, image_size], num_labels)
    else:
        labels = tf.zeros((batch_size))

    config = get_config()
    return config, pixel_values, labels


model_classes = (TFSegformerForImageClassification, TFSegformerForSemanticSegmentation)

for model_class in model_classes:
    if model_class == TFSegformerForSemanticSegmentation:
        config, pixel_values, labels = prepare_config_and_inputs(for_semseg=True)
    else:
        config, pixel_values, labels = prepare_config_and_inputs(for_semseg=False)
    
    input_for_model_fit = {"pixel_values": pixel_values, "labels": labels}

    model = model_class(config)
    model(model.dummy_inputs)
    model_weights = model.get_weights()
    
    model.compile(optimizer=tf.keras.optimizers.SGD(0.0), run_eagerly=True)
    
    history1 = model.fit(
        input_for_model_fit,
        validation_data=input_for_model_fit,
        steps_per_epoch=1,
        validation_steps=1,
        shuffle=False,
    )
    val_loss1 = history1.history["val_loss"][0]

    label_names = {"labels"}
    
    labels = {key: val for key, val in input_for_model_fit.items() if key in label_names}
    inputs_minus_labels = {key: val for key, val in input_for_model_fit.items() if key not in label_names}

    # We reinitialize the model here even though our learning rate was zero
    # because BatchNorm updates weights by means other than gradient descent.
    model.set_weights(model_weights)
    history2 = model.fit(
        inputs_minus_labels,
        labels,
        validation_data=(inputs_minus_labels, labels),
        steps_per_epoch=1,
        validation_steps=1,
        shuffle=False,
    )
    val_loss2 = history2.history["val_loss"][0]

    print(np.allclose(val_loss1, val_loss2, atol=1e-2, rtol=1e-3))
```

@amyeroberts @Rocketknight1 @sgugger 
_The documentation is not available anymore as the PR was closed or merged._<|||||>Pinging @gante as this week's TF reviewer!<|||||>> (question: why is test_keras_fit entirely overwritten?)

1. The `TFSegFormerModel` class doesn't support the fit test since we can't compute loss on embeddings. 
2. The labels for the rest of the two classes (semantic segmentation and image classification) have different label shapes. 

So, it made sense to test them in isolation. <|||||>> > (question: why is test_keras_fit entirely overwritten?)
> 
> 1. The `TFSegFormerModel` class doesn't support the fit test since we can't compute loss on embeddings.

The line `if getattr(model, "hf_compute_loss", None):` should already take care of this case, I think.

> 2. The labels for the rest of the two classes (semantic segmentation and image classification) have different label shapes.

Does the main issue come from the fact that `_prepare_for_class` in `tests/test_modeling_tf_common.py` lack the label preparation for `segmentation`?
<|||||>> Does the main issue come from the fact that _prepare_for_class in tests/test_modeling_tf_common.py lack the label preparation for segmentation?

I think so, yes. <|||||>Looks like the new `test_keras_fit()` in the base `test_modeling_tf_common` takes care of the nuances I faced when I was overriding `test_keras_fit()` (at the time of writing `modeling_tf_segformer.py`. 

So, I incorporated the latest changes, bypassing the complete rewrite. 

@ydshieh @amyeroberts @gante up for another review. <|||||>Thanks for flagging this to me!

@ydshieh @gante okay to merge? <|||||>Let gante push the final approval button 😄 <|||||>@sayakpaul our CI failed in the reworked test -- can you confirm that it runs correctly? :) 

https://github.com/huggingface/transformers/runs/7655675934?check_suite_focus=true<|||||>@gante taking a quick look [here](https://github.com/huggingface/transformers/runs/7655675934?check_suite_focus=true#step:9:139), seems like it's happening because of the second point [here](https://github.com/huggingface/transformers/pull/18412#issuecomment-1202957292). If this is the case, I will sync with @ydshieh to add support for segmentation labels in the necessary places. 

Sounds good? 