Fix LayoutLMv3 documentation

src/transformers/models/layoutlmv3/modeling_layoutlmv3.py

@@ -61,10 +61,10 @@
 
 LAYOUTLMV3_INPUTS_DOCSTRING = r"""
     Args:
-        input_ids (`torch.LongTensor` of shape `{0}`):
+        input_ids (`torch.LongTensor` of shape `({0})`):
             Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`LayoutLMv2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            {1}
+            Indices can be obtained using [`LayoutLMv3Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
             [`PreTrainedTokenizer.__call__`] for details.
 
             [What are input IDs?](../glossary#input-ids)
@@ -74,37 +74,39 @@
             config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
             format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
             y1) represents the position of the lower right corner.
-
+            {1}
         pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Batch of document images.
+            Batch of document images. Each Image is divided into patches of shape `(num_channels, config.patch_size,
+            config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to
+            `((height / config.patch_size) * (width / config.patch_size))`.
 
-        attention_mask (`torch.FloatTensor` of shape `{0}`, *optional*):
+        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
             Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
 
             - 1 for tokens that are **not masked**,
             - 0 for tokens that are **masked**.
-
+            {1}
             [What are attention masks?](../glossary#attention-mask)
-        token_type_ids (`torch.LongTensor` of shape `{0}`, *optional*):
+        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
             Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
             1]`:
 
             - 0 corresponds to a *sentence A* token,
             - 1 corresponds to a *sentence B* token.
-
+            {1}
             [What are token type IDs?](../glossary#token-type-ids)
-        position_ids (`torch.LongTensor` of shape `{0}`, *optional*):
+        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
             Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
             config.max_position_embeddings - 1]`.
-
+            {1}
             [What are position IDs?](../glossary#position-ids)
         head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
             Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
 
             - 1 indicates the head is **not masked**,
             - 0 indicates the head is **masked**.
 
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
             Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
             is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
             model's internal embedding lookup matrix.
@@ -118,6 +120,11 @@
             Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
 """
 
+LAYOUTLMV3MODEL_INPUTS_DOCSTRING = r"""
+            Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for
+            [CLS] token. See `pixel_values` for `patch_sequence_length`.
+"""
+
 
 class LayoutLMv3PatchEmbeddings(nn.Module):
     """LayoutLMv3 image (patch) embeddings. This class also automatically interpolates the position embeddings for varying
@@ -763,7 +770,9 @@ def forward_image(self, pixel_values):
 
         return embeddings
 
-    @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_start_docstrings_to_model_forward(
+        LAYOUTLMV3_INPUTS_DOCSTRING.format("batch_size, token_sequence_length", LAYOUTLMV3MODEL_INPUTS_DOCSTRING)
+    )
     @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -975,7 +984,7 @@ def __init__(self, config):
 
         self.init_weights()
 
-    @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING.format("batch_size, sequence_length", ""))
     @replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1084,7 +1093,7 @@ def __init__(self, config):
 
         self.init_weights()
 
-    @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING.format("batch_size, sequence_length", ""))
     @replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
@@ -1214,7 +1223,7 @@ def __init__(self, config):
 
         self.init_weights()
 
-    @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING.format("batch_size, sequence_length", ""))
     @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,