Type hints MCTCT (#19618)

* add type hints to mctct

* run auto style corrections

* change torch.bool to bool#

* Update src/transformers/models/mctct/modeling_mctct.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

* Remove optional tags for attention_mask and head_mask'

* fix optional tags'

* Update src/transformers/models/mctct/modeling_mctct.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

src/transformers/models/mctct/modeling_mctct.py

@@ -17,7 +17,7 @@
 
 import math
 import random
-from typing import Optional
+from typing import Optional, Tuple, Union
 
 import torch
 import torch.utils.checkpoint
@@ -566,13 +566,13 @@ class MCTCTEncoder(MCTCTPreTrainedModel):
 
     def forward(
         self,
-        input_features,
-        attention_mask,
-        head_mask,
-        output_attentions=False,
-        output_hidden_states=False,
-        return_dict=True,
-    ):
+        input_features: torch.Tensor,
+        attention_mask: torch.Tensor,
+        head_mask: torch.Tensor,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ) -> Union[Tuple, BaseModelOutput]:
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -680,13 +680,13 @@ class MCTCTModel(MCTCTPreTrainedModel):
     )
     def forward(
         self,
-        input_features,
-        attention_mask=None,
-        head_mask=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
+        input_features: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -751,14 +751,14 @@ class MCTCTForCTC(MCTCTPreTrainedModel):
     )
     def forward(
         self,
-        input_features,
-        attention_mask=None,
-        head_mask=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        labels=None,
-    ):
+        input_features: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, CausalLMOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
             Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
@@ -783,7 +783,6 @@ class MCTCTForCTC(MCTCTPreTrainedModel):
 
         loss = None
         if labels is not None:
-
             if labels.max() >= self.config.vocab_size:
                 raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")