Added Type Hints for modeling_tf_encoder_decoder.py (#21673)

* Ran Black formatting

* Added imports and reformatted

* Update src/transformers/models/encoder_decoder/modeling_tf_encoder_decoder.py

---------
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

src/transformers/models/encoder_decoder/modeling_tf_encoder_decoder.py

@@ -19,13 +19,20 @@ import gc
 import os
 import tempfile
 import warnings
-from typing import Optional
+from typing import Optional, Tuple, Union
 
+import numpy as np
 import tensorflow as tf
 
 from ...configuration_utils import PretrainedConfig
 from ...modeling_tf_outputs import TFBaseModelOutput, TFSeq2SeqLMOutput
-from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFPreTrainedModel, get_initializer, unpack_inputs
+from ...modeling_tf_utils import (
+    TFCausalLanguageModelingLoss,
+    TFModelInputType,
+    TFPreTrainedModel,
+    get_initializer,
+    unpack_inputs,
+)
 from ...tf_utils import shape_list
 from ...utils import (
     DUMMY_INPUTS,
@@ -509,22 +516,22 @@ class TFEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss):
     @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
     def call(
         self,
-        input_ids=None,
-        attention_mask=None,
-        decoder_input_ids=None,
-        decoder_attention_mask=None,
-        encoder_outputs=None,
-        past_key_values=None,
-        inputs_embeds=None,
-        decoder_inputs_embeds=None,
-        labels=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        training=False,
+        input_ids: Optional[TFModelInputType] = None,
+        attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        decoder_input_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        decoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        encoder_outputs: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        past_key_values: Optional[Tuple[Tuple[tf.Tensor]]] = None,
+        inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        decoder_inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
         **kwargs,
-    ):
+    ) -> Union[TFSeq2SeqLMOutput, Tuple[tf.Tensor]]:
         r"""
         Returns:
 
@@ -718,3 +725,7 @@ class TFEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss):
             " respective methods of the wrapped objects (model.encoder.resize_token_embeddings(...) or"
             " model.decoder.resize_token_embeddings(...))"
         )
+
+    def _reorder_cache(self, past, beam_idx):
+        # apply decoder cache reordering here
+        return self.decoder._reorder_cache(past, beam_idx)