text2text, summarization and translation

src/transformers/pipelines/text2text_generation.py

@@ -89,7 +89,16 @@ def _sanitize_parameters(
         forward_params = generate_kwargs
 
         postprocess_params = {}
+        if return_text is not None:
+            warnings.warn(
+                "The `return_text` argument is deprecated and will be removed in version 5 of Transformers. ",
+                FutureWarning,
+            )
         if return_tensors is not None and return_type is None:
+            warnings.warn(
+                "The `return_tensors` argument is deprecated and will be removed in version 5 of Transformers. ",
+                FutureWarning,
+            )
             return_type = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
         if return_type is not None:
             postprocess_params["return_type"] = return_type
@@ -135,17 +144,13 @@ def _parse_and_tokenize(self, *args, truncation):
             del inputs["token_type_ids"]
         return inputs
 
-    def __call__(self, *args, **kwargs):
+    def __call__(self, *inputs, **kwargs):
         r"""
         Generate the output text(s) using text(s) given as inputs.
 
         Args:
-            args (`str` or `List[str]`):
+            inputs (`str` or `List[str]`):
                 Input text for the encoder.
-            return_tensors (`bool`, *optional*, defaults to `False`):
-                Whether or not to include the tensors of predictions (as token indices) in the outputs.
-            return_text (`bool`, *optional*, defaults to `True`):
-                Whether or not to include the decoded texts in the outputs.
             clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
                 Whether or not to clean up the potential extra spaces in the text output.
             truncation (`TruncationStrategy`, *optional*, defaults to `TruncationStrategy.DO_NOT_TRUNCATE`):
@@ -164,10 +169,10 @@ def __call__(self, *args, **kwargs):
               ids of the generated text.
         """
 
-        result = super().__call__(*args, **kwargs)
+        result = super().__call__(*inputs, **kwargs)
         if (
-            isinstance(args[0], list)
-            and all(isinstance(el, str) for el in args[0])
+            isinstance(inputs[0], list)
+            and all(isinstance(el, str) for el in inputs[0])
             and all(len(res) == 1 for res in result)
         ):
             return [res[0] for res in result]
@@ -252,14 +257,14 @@ def __call__(self, *args, **kwargs):
         Summarize the text(s) given as inputs.
 
         Args:
-            documents (*str* or `List[str]`):
+            inputs (*str* or `List[str]`):
                 One or several articles (or one list of articles) to summarize.
-            return_text (`bool`, *optional*, defaults to `True`):
-                Whether or not to include the decoded texts in the outputs
-            return_tensors (`bool`, *optional*, defaults to `False`):
-                Whether or not to include the tensors of predictions (as token indices) in the outputs.
             clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
                 Whether or not to clean up the potential extra spaces in the text output.
+            truncation (`TruncationStrategy`, *optional*, defaults to `TruncationStrategy.DO_NOT_TRUNCATE`):
+                The truncation strategy for the tokenization within the pipeline. `TruncationStrategy.DO_NOT_TRUNCATE`
+                (default) will never truncate, but it is sometimes desirable to truncate the input to fit the model's
+                max_length instead of throwing an error down the line.
             generate_kwargs:
                 Additional keyword arguments to pass along to the generate method of the model (see the generate method
                 corresponding to your framework [here](./text_generation)).
@@ -343,19 +348,19 @@ def _sanitize_parameters(self, src_lang=None, tgt_lang=None, **kwargs):
                 preprocess_params["tgt_lang"] = items[3]
         return preprocess_params, forward_params, postprocess_params
 
-    def __call__(self, *args, **kwargs):
+    def __call__(self, *inputs, **kwargs):
         r"""
         Translate the text(s) given as inputs.
 
         Args:
-            args (`str` or `List[str]`):
+            inputs (`str` or `List[str]`):
                 Texts to be translated.
-            return_tensors (`bool`, *optional*, defaults to `False`):
-                Whether or not to include the tensors of predictions (as token indices) in the outputs.
-            return_text (`bool`, *optional*, defaults to `True`):
-                Whether or not to include the decoded texts in the outputs.
             clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
                 Whether or not to clean up the potential extra spaces in the text output.
+            truncation (`TruncationStrategy`, *optional*, defaults to `TruncationStrategy.DO_NOT_TRUNCATE`):
+                The truncation strategy for the tokenization within the pipeline. `TruncationStrategy.DO_NOT_TRUNCATE`
+                (default) will never truncate, but it is sometimes desirable to truncate the input to fit the model's
+                max_length instead of throwing an error down the line.
             src_lang (`str`, *optional*):
                 The language of the input. Might be required for multilingual models. Will not have any effect for
                 single pair translation models
@@ -373,4 +378,4 @@ def __call__(self, *args, **kwargs):
             - **translation_token_ids** (`torch.Tensor` or `tf.Tensor`, present when `return_tensors=True`) -- The
               token ids of the translation.
         """
-        return super().__call__(*args, **kwargs)
+        return super().__call__(*inputs, **kwargs)