Better booleans handling in the TF models (#8777)

* Apply on BERT and ALBERT

* Update TF Bart

* Add input processing to TF BART

* Add input processing for TF CTRL

* Add input processing to TF Distilbert

* Add input processing to TF DPR

* Add input processing to TF Electra

* Add deprecated arguments

* Add input processing to TF XLM

* Add input processing to TF Funnel

* Add input processing to TF GPT2

* Add input processing to TF Longformer

* Add input processing to TF Lxmert

* Apply style

* Add input processing to TF Mobilebert

* Add input processing to TF GPT

* Add input processing to TF Roberta

* Add input processing to TF T5

* Add input processing to TF TransfoXL

* Apply style

* Rebase on master

* Bug fix

* Retry to bugfix

* Retry bug fix

* Fix wrong model name

* Try another fix

* Fix BART

* Fix input precessing

* Apply style

* Put the deprecated warnings in the input processing function

* Remove the unused imports

* Raise an error when len(kwargs)>0

* test ModelOutput instead of TFBaseModelOutput

* Bug fix

* Address Patrick's comments

* Address Patrick's comments

* Address Sylvain's comments

* Add boolean processing for the inputs

* Apply style

* Missing optional

* Fix missing some input proc

* Update the template

* Fix missing inputs

* Missing input

* Fix args parameter

* Trigger CI

* Trigger CI

* Trigger CI

* Address Patrick's and Sylvain's comments

* Replace warn by warning

* Trigger CI

* Fix XLNET

* Fix detection

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py

@@ -474,6 +474,7 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
 
+        self.config = config
         self.num_hidden_layers = config.num_hidden_layers
         self.initializer_range = config.initializer_range
         self.output_attentions = config.output_attentions
@@ -513,6 +514,7 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
     ):
         inputs = input_processing(
             func=self.call,
+            config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
@@ -525,14 +527,7 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
             training=training,
             kwargs_call=kwargs,
         )
-        output_attentions = (
-            inputs["output_attentions"] if inputs["output_attentions"] is not None else self.output_attentions
-        )
-        output_hidden_states = (
-            inputs["output_hidden_states"] if inputs["output_hidden_states"] is not None else self.output_hidden_states
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.return_dict
-
+        
         if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif inputs["input_ids"] is not None:
@@ -585,9 +580,9 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
             embedding_output,
             extended_attention_mask,
             inputs["head_mask"],
-            output_attentions,
-            output_hidden_states,
-            return_dict,
+            inputs["output_attentions"],
+            inputs["output_hidden_states"],
+            inputs["return_dict"],
             training=inputs["training"],
         )
 
@@ -740,6 +735,7 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod
     ):
         inputs = input_processing(
             func=self.call,
+            config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
@@ -817,6 +813,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
         """
         inputs = input_processing(
             func=self.call,
+            config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
@@ -830,7 +827,6 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
             training=training,
             kwargs_call=kwargs,
         )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.{{cookiecutter.lowercase_modelname}}.return_dict
         outputs = self.{{cookiecutter.lowercase_modelname}}(
             inputs["input_ids"],
             attention_mask=inputs["attention_mask"],
@@ -840,7 +836,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
             inputs_embeds=inputs["inputs_embeds"],
             output_attentions=inputs["output_attentions"],
             output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
+            return_dict=inputs["return_dict"],
             training=inputs["training"],
         )
 
@@ -848,7 +844,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
         prediction_scores = self.mlm(sequence_output, training=inputs["training"])
         loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], prediction_scores)
 
-        if not return_dict:
+        if not inputs["return_dict"]:
             output = (prediction_scores,) + outputs[1:]
             return ((loss,) + output) if loss is not None else output
 
@@ -930,6 +926,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookie
         """
         inputs = input_processing(
             func=self.call,
+            config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
@@ -943,7 +940,6 @@ class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookie
             training=training,
             kwargs_call=kwargs,
         )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.{{cookiecutter.lowercase_modelname}}.return_dict
         outputs = self.{{cookiecutter.lowercase_modelname}}(
             inputs["input_ids"],
             attention_mask=inputs["attention_mask"],
@@ -953,13 +949,13 @@ class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookie
             inputs_embeds=inputs["inputs_embeds"],
             output_attentions=inputs["output_attentions"],
             output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
+            return_dict=inputs["return_dict"],
             training=inputs["training"],
         )
         logits = self.classifier(outputs[0], training=inputs["training"])
         loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
 
-        if not return_dict:
+        if not inputs["return_dict"]:
             output = (logits,) + outputs[1:]
 
             return ((loss,) + output) if loss is not None else output
@@ -1029,6 +1025,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c
         """
         inputs = input_processing(
             func=self.call,
+            config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
@@ -1042,8 +1039,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c
             training=training,
             kwargs_call=kwargs,
         )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.{{cookiecutter.lowercase_modelname}}.config.return_dict
-
+        
         if inputs["input_ids"] is not None:
             num_choices = shape_list(inputs["input_ids"])[1]
             seq_length = shape_list(inputs["input_ids"])[2]
@@ -1075,7 +1071,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c
             flat_inputs_embeds,
             inputs["output_attentions"],
             inputs["output_hidden_states"],
-            return_dict=return_dict,
+            return_dict=inputs["return_dict"],
             training=inputs["training"],
         )
         logits = self.sequence_summary(outputs[0], training=inputs["training"])
@@ -1083,7 +1079,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c
         reshaped_logits = tf.reshape(logits, (-1, num_choices))
         loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], reshaped_logits)
 
-        if not return_dict:
+        if not inputs["return_dict"]:
             output = (reshaped_logits,) + outputs[1:]
 
             return ((loss,) + output) if loss is not None else output
@@ -1141,6 +1137,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecut
         """
         inputs = input_processing(
             func=self.call,
+            config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
@@ -1154,7 +1151,6 @@ class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecut
             training=training,
             kwargs_call=kwargs,
         )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.{{cookiecutter.uppercase_modelname}}.return_dict
         outputs = self.{{cookiecutter.uppercase_modelname}}(
             inputs["input_ids"],
             attention_mask=inputs["attention_mask"],
@@ -1164,7 +1160,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecut
             inputs_embeds=inputs["inputs_embeds"],
             output_attentions=inputs["output_attentions"],
             output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
+            return_dict=inputs["return_dict"],
             training=inputs["training"],
         )
         sequence_output = outputs[0]
@@ -1172,7 +1168,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecut
         logits = self.classifier(sequence_output)
         loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
 
-        if not return_dict:
+        if not inputs["return_dict"]:
             output = (logits,) + outputs[1:]
             return ((loss,) + output) if loss is not None else output
 
@@ -1235,6 +1231,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte
         """
         inputs = input_processing(
             func=self.call,
+            config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
@@ -1249,7 +1246,6 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte
             training=training,
             kwargs_call=kwargs,
         )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.{{cookiecutter.uppercase_modelname}}.return_dict
         outputs = self.{{cookiecutter.uppercase_modelname}}(
             inputs["input_ids"],
             attention_mask=inputs["attention_mask"],
@@ -1259,7 +1255,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte
             inputs_embeds=inputs["inputs_embeds"],
             output_attentions=inputs["output_attentions"],
             output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
+            return_dict=inputs["return_dict"],
             training=inputs["training"],
         )
         sequence_output = outputs[0]
@@ -1274,7 +1270,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte
             labels["end_position"] = inputs["end_positions"]
             loss = self.compute_loss(labels, (start_logits, end_logits))
 
-        if not return_dict:
+        if not inputs["return_dict"]:
             output = (start_logits, end_logits) + outputs[1:]
             return ((loss,) + output) if loss is not None else output