Fix doctest (#20843)

* fix doc for generation, dinat, nat and prelayernorm

* style

* update

* fix cpies

* use auto config and auto tokenizer
Co-authored-by: sgugger <sylvain.gugger@gmail.com>

* als modify roberta and the depending models
Co-authored-by: sgugger <sylvain.gugger@gmail.com>

src/transformers/generation/utils.py

@@ -2264,6 +2264,7 @@ class GenerationMixin:
 
         >>> # set pad_token_id to eos_token_id because GPT2 does not have a EOS token
         >>> model.config.pad_token_id = model.config.eos_token_id
+        >>> model.generation_config.pad_token_id = model.config.eos_token_id
 
         >>> input_prompt = "Today is a beautiful day, and"
         >>> input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids

src/transformers/models/camembert/modeling_camembert.py

@@ -1502,11 +1502,11 @@ class CamembertForCausalLM(CamembertPreTrainedModel):
         Example:
 
         ```python
-        >>> from transformers import CamembertTokenizer, CamembertForCausalLM, CamembertConfig
+        >>> from transformers import AutoTokenizer, CamembertForCausalLM, AutoConfig
         >>> import torch
 
-        >>> tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
-        >>> config = CamembertConfig.from_pretrained("camembert-base")
+        >>> tokenizer = AutoTokenizer.from_pretrained("camembert-base")
+        >>> config = AutoConfig.from_pretrained("camembert-base")
         >>> config.is_decoder = True
         >>> model = CamembertForCausalLM.from_pretrained("camembert-base", config=config)
 

src/transformers/models/dinat/modeling_dinat.py

@@ -943,7 +943,7 @@ class DinatBackbone(DinatPreTrainedModel, BackboneMixin):
 
         >>> processor = AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224")
         >>> model = AutoBackbone.from_pretrained(
-        ...     "shi-labs/nat-mini-in1k-2240", out_features=["stage1", "stage2", "stage3", "stage4"]
+        ...     "shi-labs/nat-mini-in1k-224", out_features=["stage1", "stage2", "stage3", "stage4"]
         ... )
 
         >>> inputs = processor(image, return_tensors="pt")
@@ -952,7 +952,7 @@ class DinatBackbone(DinatPreTrainedModel, BackboneMixin):
 
         >>> feature_maps = outputs.feature_maps
         >>> list(feature_maps[-1].shape)
-        [1, 2048, 7, 7]
+        [1, 512, 7, 7]
         ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
         output_hidden_states = (

src/transformers/models/nat/modeling_nat.py

@@ -921,7 +921,7 @@ class NatBackbone(NatPreTrainedModel, BackboneMixin):
 
         >>> processor = AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224")
         >>> model = AutoBackbone.from_pretrained(
-        ...     "shi-labs/nat-mini-in1k-2240", out_features=["stage1", "stage2", "stage3", "stage4"]
+        ...     "shi-labs/nat-mini-in1k-224", out_features=["stage1", "stage2", "stage3", "stage4"]
         ... )
 
         >>> inputs = processor(image, return_tensors="pt")
@@ -930,7 +930,7 @@ class NatBackbone(NatPreTrainedModel, BackboneMixin):
 
         >>> feature_maps = outputs.feature_maps
         >>> list(feature_maps[-1].shape)
-        [1, 2048, 7, 7]
+        [1, 512, 7, 7]
         ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
         output_hidden_states = (

src/transformers/models/roberta/modeling_roberta.py

@@ -956,11 +956,11 @@ class RobertaForCausalLM(RobertaPreTrainedModel):
         Example:
 
         ```python
-        >>> from transformers import RobertaTokenizer, RobertaForCausalLM, RobertaConfig
+        >>> from transformers import AutoTokenizer, RobertaForCausalLM, AutoConfig
         >>> import torch
 
-        >>> tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
-        >>> config = RobertaConfig.from_pretrained("roberta-base")
+        >>> tokenizer = AutoTokenizer.from_pretrained("roberta-base")
+        >>> config = AutoConfig.from_pretrained("roberta-base")
         >>> config.is_decoder = True
         >>> model = RobertaForCausalLM.from_pretrained("roberta-base", config=config)
 

src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py

@@ -885,7 +885,7 @@ class RobertaPreLayerNormModel(RobertaPreLayerNormPreTrainedModel):
     """RoBERTa-PreLayerNorm Model with a `language modeling` head on top for CLM fine-tuning.""",
     ROBERTA_PRELAYERNORM_START_DOCSTRING,
 )
-# Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM with roberta-base->andreasmadsen/efficient_mlm_m0.40,ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm
+# Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM with roberta-base->andreasmadsen/efficient_mlm_m0.40,ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm, RobertaPreLayerNormTokenizer->RobertaTokenizer
 class RobertaPreLayerNormForCausalLM(RobertaPreLayerNormPreTrainedModel):
     _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
     _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
@@ -963,15 +963,11 @@ class RobertaPreLayerNormForCausalLM(RobertaPreLayerNormPreTrainedModel):
         Example:
 
         ```python
-        >>> from transformers import (
-        ...     RobertaPreLayerNormTokenizer,
-        ...     RobertaPreLayerNormForCausalLM,
-        ...     RobertaPreLayerNormConfig,
-        ... )
+        >>> from transformers import AutoTokenizer, RobertaPreLayerNormForCausalLM, AutoConfig
         >>> import torch
 
-        >>> tokenizer = RobertaPreLayerNormTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
-        >>> config = RobertaPreLayerNormConfig.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
+        >>> tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
+        >>> config = AutoConfig.from_pretrained("andreasmadsen/efficient_mlm_m0.40")
         >>> config.is_decoder = True
         >>> model = RobertaPreLayerNormForCausalLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40", config=config)
 

src/transformers/models/xlm_roberta/modeling_xlm_roberta.py

@@ -960,11 +960,11 @@ class XLMRobertaForCausalLM(XLMRobertaPreTrainedModel):
         Example:
 
         ```python
-        >>> from transformers import XLMRobertaTokenizer, XLMRobertaForCausalLM, XLMRobertaConfig
+        >>> from transformers import AutoTokenizer, XLMRobertaForCausalLM, AutoConfig
         >>> import torch
 
-        >>> tokenizer = XLMRobertaTokenizer.from_pretrained("roberta-base")
-        >>> config = XLMRobertaConfig.from_pretrained("roberta-base")
+        >>> tokenizer = AutoTokenizer.from_pretrained("roberta-base")
+        >>> config = AutoConfig.from_pretrained("roberta-base")
         >>> config.is_decoder = True
         >>> model = XLMRobertaForCausalLM.from_pretrained("roberta-base", config=config)