s|Pretrained|PreTrained| (#11048)

examples/research_projects/rag/distributed_pytorch_retriever.py

@@ -22,10 +22,10 @@ class RagPyTorchDistributedRetriever(RagRetriever):
     Args:
         config (:class:`~transformers.RagConfig`):
             The configuration of the RAG model this Retriever is used with. Contains parameters indicating which ``Index`` to build.
-        question_encoder_tokenizer (:class:`~transformers.PretrainedTokenizer`):
+        question_encoder_tokenizer (:class:`~transformers.PreTrainedTokenizer`):
             The tokenizer that was used to tokenize the question.
             It is used to decode the question and then use the generator_tokenizer.
-        generator_tokenizer (:class:`~transformers.PretrainedTokenizer`):
+        generator_tokenizer (:class:`~transformers.PreTrainedTokenizer`):
             The tokenizer used for the generator part of the RagModel.
         index (:class:`~transformers.models.rag.retrieval_rag.Index`, optional, defaults to the one defined by the configuration):
             If specified, use this index instead of the one built using the configuration

examples/research_projects/rag/distributed_ray_retriever.py

@@ -50,10 +50,10 @@ class RagRayDistributedRetriever(RagRetriever):
     Args:
         config (:class:`~transformers.RagConfig`):
             The configuration of the RAG model this Retriever is used with. Contains parameters indicating which ``Index`` to build.
-        question_encoder_tokenizer (:class:`~transformers.PretrainedTokenizer`):
+        question_encoder_tokenizer (:class:`~transformers.PreTrainedTokenizer`):
             The tokenizer that was used to tokenize the question.
             It is used to decode the question and then use the generator_tokenizer.
-        generator_tokenizer (:class:`~transformers.PretrainedTokenizer`):
+        generator_tokenizer (:class:`~transformers.PreTrainedTokenizer`):
             The tokenizer used for the generator part of the RagModel.
         retrieval_workers (:obj:`List[ray.ActorClass(RayRetriever)]`): A list of already initialized `RayRetriever` actors.
             These actor classes run on remote processes and are responsible for performing the index lookup.

src/transformers/generation_beam_search.py

@@ -27,7 +27,7 @@ PROCESS_INPUTS_DOCSTRING = r"""
         input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size * num_beams, sequence_length)`):
             Indices of input sequence tokens in the vocabulary.
 
-            Indices can be obtained using any class inheriting from :class:`~transformers.PretrainedTokenizer`. See
+            Indices can be obtained using any class inheriting from :class:`~transformers.PreTrainedTokenizer`. See
             :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
             details.
 
@@ -60,7 +60,7 @@ FINALIZE_INPUTS_DOCSTRING = r"""
         input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size * num_beams, sequence_length)`):
             Indices of input sequence tokens in the vocabulary.
 
-            Indices can be obtained using any class inheriting from :class:`~transformers.PretrainedTokenizer`. See
+            Indices can be obtained using any class inheriting from :class:`~transformers.PreTrainedTokenizer`. See
             :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
             details.
 
@@ -86,8 +86,8 @@ FINALIZE_INPUTS_DOCSTRING = r"""
 
 class BeamScorer(ABC):
     """
-    Abstract base class for all beam scorers that are used for :meth:`~transformers.PretrainedModel.beam_search` and
+    Abstract base class for all beam scorers that are used for :meth:`~transformers.PreTrainedModel.beam_search` and
-    :meth:`~transformers.PretrainedModel.beam_sample`.
+    :meth:`~transformers.PreTrainedModel.beam_sample`.
     """
 
     @abstractmethod

src/transformers/generation_logits_process.py

@@ -474,7 +474,7 @@ class PrefixConstrainedLogitsProcessor(LogitsProcessor):
 class HammingDiversityLogitsProcessor(LogitsProcessor):
     r"""
     :class:`transformers.LogitsProcessor` that enforces diverse beam search. Note that this logits processor is only
-    effective for :meth:`transformers.PretrainedModel.group_beam_search`. See `Diverse Beam Search: Decoding Diverse
+    effective for :meth:`transformers.PreTrainedModel.group_beam_search`. See `Diverse Beam Search: Decoding Diverse
     Solutions from Neural Sequence Models <https://arxiv.org/pdf/1610.02424.pdf>`__ for more details.
 
     Args:

src/transformers/models/ctrl/modeling_ctrl.py

@@ -586,7 +586,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
     def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
         """
         This function is used to re-order the :obj:`past_key_values` cache if
-        :meth:`~transformers.PretrainedModel.beam_search` or :meth:`~transformers.PretrainedModel.beam_sample` is
+        :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
         called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
         """
         return tuple(

src/transformers/models/encoder_decoder/modeling_encoder_decoder.py

@@ -89,7 +89,7 @@ ENCODER_DECODER_INPUTS_DOCSTRING = r"""
             :obj:`past_key_values`).
 
             Provide for sequence to sequence training to the decoder. Indices can be obtained using
-            :class:`~transformers.PretrainedTokenizer`. See :meth:`transformers.PreTrainedTokenizer.encode` and
+            :class:`~transformers.PreTrainedTokenizer`. See :meth:`transformers.PreTrainedTokenizer.encode` and
             :meth:`transformers.PreTrainedTokenizer.__call__` for details.
         decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
             Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will

src/transformers/models/gpt2/modeling_gpt2.py

@@ -951,7 +951,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
     def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
         """
         This function is used to re-order the :obj:`past_key_values` cache if
-        :meth:`~transformers.PretrainedModel.beam_search` or :meth:`~transformers.PretrainedModel.beam_sample` is
+        :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
         called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
         """
         return tuple(
@@ -1157,7 +1157,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
     def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
         """
         This function is used to re-order the :obj:`past_key_values` cache if
-        :meth:`~transformers.PretrainedModel.beam_search` or :meth:`~transformers.PretrainedModel.beam_sample` is
+        :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
         called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
         """
         return tuple(

src/transformers/models/transfo_xl/modeling_transfo_xl.py

@@ -1141,8 +1141,8 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
     @staticmethod
     def _reorder_cache(mems: List[torch.Tensor], beam_idx: torch.Tensor) -> List[torch.Tensor]:
         """
-        This function is used to re-order the :obj:`mems` cache if :meth:`~transformers.PretrainedModel.beam_search` or
+        This function is used to re-order the :obj:`mems` cache if :meth:`~transformers.PreTrainedModel.beam_search` or
-        :meth:`~transformers.PretrainedModel.beam_sample` is called. This is required to match :obj:`mems` with the
+        :meth:`~transformers.PreTrainedModel.beam_sample` is called. This is required to match :obj:`mems` with the
         correct beam_idx at every generation step.
         """
         return [layer_past.index_select(1, beam_idx.to(layer_past.device)) for layer_past in mems]

src/transformers/models/xlnet/modeling_xlnet.py

@@ -1470,8 +1470,8 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
     @staticmethod
     def _reorder_cache(mems: List[torch.Tensor], beam_idx: torch.Tensor) -> List[torch.Tensor]:
         """
-        This function is used to re-order the :obj:`mems` cache if :meth:`~transformers.PretrainedModel.beam_search` or
+        This function is used to re-order the :obj:`mems` cache if :meth:`~transformers.PreTrainedModel.beam_search` or
-        :meth:`~transformers.PretrainedModel.beam_sample` is called. This is required to match :obj:`mems` with the
+        :meth:`~transformers.PreTrainedModel.beam_sample` is called. This is required to match :obj:`mems` with the
         correct beam_idx at every generation step.
         """
         return [layer_past.index_select(1, beam_idx.to(layer_past.device)) for layer_past in mems]

src/transformers/pipelines/__init__.py

@@ -351,7 +351,7 @@ def pipeline(
             # Impossible to guest what is the right tokenizer here
             raise Exception(
                 "Impossible to guess which tokenizer to use. "
-                "Please provided a PretrainedTokenizer class or a path/identifier to a pretrained tokenizer."
+                "Please provided a PreTrainedTokenizer class or a path/identifier to a pretrained tokenizer."
             )
 
     modelcard = None

src/transformers/tokenization_utils_base.py

@@ -1930,7 +1930,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
         """
         if not legacy_format:
             raise ValueError(
-                "Only fast tokenizers (instances of PretrainedTokenizerFast) can be saved in non legacy format."
+                "Only fast tokenizers (instances of PreTrainedTokenizerFast) can be saved in non legacy format."
             )
 
         save_directory = str(save_directory)