Fixed datatype related issues in DataCollatorForLanguageModeling (#36457)

Fixed 2 issues regarding `tests/trainer/test_data_collator.py::TFDataCollatorIntegrationTest::test_all_mask_replacement`:
1. I got the error `RuntimeError: "bernoulli_tensor_cpu_p_" not implemented for 'Long'`. This is because the `mask_replacement_prob=1` and `torch.bernoulli` doesn't accept this type (which would be a `torch.long` dtype instead. I fixed this by manually casting the probability arguments in the `__post_init__` function of `DataCollatorForLanguageModeling`.
2. I also got the error `tensorflow.python.framework.errors_impl.InvalidArgumentError: cannot compute Equal as input #1(zero-based) was expected to be a int64 tensor but is a int32 tensor [Op:Equal]` due to the line `tf.reduce_all((batch["input_ids"] == inputs) | (batch["input_ids"] == tokenizer.mask_token_id))` in `test_data_collator.py`. This occurs because the type of the `inputs` variable is `tf.int32`. Solved this by manually casting it to `tf.int64` in the test, as the expected return type of `batch["input_ids"]` is `tf.int64`.

src/transformers/data/data_collator.py

@@ -843,6 +843,10 @@ class DataCollatorForLanguageModeling(DataCollatorMixin):
         if self.random_replace_prob < 0 or self.random_replace_prob > 1:
             raise ValueError("random_replace_prob should be between 0 and 1.")
 
+        self.mlm_probability = float(self.mlm_probability)
+        self.mask_replace_prob = float(self.mask_replace_prob)
+        self.random_replace_prob = float(self.random_replace_prob)
+
         if self.tf_experimental_compile:
             import tensorflow as tf
 

tests/trainer/test_data_collator.py

@@ -1052,7 +1052,9 @@ class TFDataCollatorIntegrationTest(unittest.TestCase):
 
         # confirm that every token is either the original token or [MASK]
         self.assertTrue(
-            tf.reduce_all((batch["input_ids"] == inputs) | (batch["input_ids"] == tokenizer.mask_token_id))
+            tf.reduce_all(
+                (batch["input_ids"] == tf.cast(inputs, tf.int64)) | (batch["input_ids"] == tokenizer.mask_token_id)
+            )
         )
 
         # numpy call