Added type hints for Reformer (#16175)

src/transformers/models/reformer/modeling_reformer.py

@@ -20,7 +20,7 @@ from collections import namedtuple
 from dataclasses import dataclass
 from functools import reduce
 from operator import mul
-from typing import List, Optional, Tuple
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -1995,18 +1995,18 @@ class ReformerModel(ReformerPreTrainedModel):
     )
     def forward(
         self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        num_hashes=None,
-        past_buckets_states=None,
-        use_cache=None,
-        output_hidden_states=None,
-        output_attentions=None,
-        return_dict=None,
-    ):
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        num_hashes: Optional[int] = None,
+        past_buckets_states: Optional[List[Tuple[torch.Tensor]]] = None,
+        use_cache: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, ReformerModelOutput]:
         use_cache = use_cache if use_cache is not None else self.config.use_cache
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -2202,19 +2202,19 @@ class ReformerModelWithLMHead(ReformerPreTrainedModel):
     )
     def forward(
         self,
-        input_ids=None,
-        position_ids=None,
-        attention_mask=None,
-        head_mask=None,
-        inputs_embeds=None,
-        num_hashes=None,
-        past_buckets_states=None,
-        use_cache=None,
-        output_hidden_states=None,
-        output_attentions=None,
-        return_dict=None,
-        labels=None,
-    ):
+        input_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        num_hashes: Optional[int] = None,
+        past_buckets_states: Optional[List[Tuple[torch.Tensor]]] = None,
+        use_cache: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.Tensor] = None,
+    ) -> Union[Tuple, CausalLMOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
                 Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
@@ -2318,17 +2318,17 @@ class ReformerForMaskedLM(ReformerPreTrainedModel):
     )
     def forward(
         self,
-        input_ids=None,
-        position_ids=None,
-        attention_mask=None,
-        head_mask=None,
-        inputs_embeds=None,
-        num_hashes=None,
-        labels=None,
-        output_hidden_states=None,
-        output_attentions=None,
-        return_dict=None,
-    ):
+        input_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        num_hashes: Optional[int] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MaskedLMOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                 Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
@@ -2400,17 +2400,17 @@ class ReformerForSequenceClassification(ReformerPreTrainedModel):
     )
     def forward(
         self,
-        input_ids=None,
-        position_ids=None,
-        attention_mask=None,
-        head_mask=None,
-        inputs_embeds=None,
-        num_hashes=None,
-        labels=None,
-        output_hidden_states=None,
-        output_attentions=None,
-        return_dict=None,
-    ):
+        input_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        num_hashes: Optional[int] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
@@ -2519,18 +2519,18 @@ class ReformerForQuestionAnswering(ReformerPreTrainedModel):
     )
     def forward(
         self,
-        input_ids=None,
-        position_ids=None,
-        attention_mask=None,
-        head_mask=None,
-        inputs_embeds=None,
-        num_hashes=None,
-        start_positions=None,
-        end_positions=None,
-        output_hidden_states=None,
-        output_attentions=None,
-        return_dict=None,
-    ):
+        input_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        num_hashes: Optional[int] = None,
+        start_positions: Optional[torch.Tensor] = None,
+        end_positions: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
         r"""
         start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for position (index) of the start of the labelled span for computing the token classification loss.