Reorganize repo (#8580)

* Put models in subfolders

* Styling

* Fix imports in tests

* More fixes in test imports

* Sneaky hidden imports

* Fix imports in doc files

* More sneaky imports

* Finish fixing tests

* Fix examples

* Fix path for copies

* More fixes for examples

* Fix dummy files

* More fixes for example

* More model import fixes

* Is this why you're unhappy GitHub?

* Fix imports in conver command

src/transformers/retrieval_rag.py → src/transformers/models/rag/retrieval_rag.py

@@ -21,8 +21,7 @@ from typing import Iterable, List, Optional, Tuple
 
 import numpy as np
 
-from .configuration_rag import RagConfig
-from .file_utils import (
+from ...file_utils import (
     cached_path,
     is_datasets_available,
     is_faiss_available,
@@ -30,9 +29,10 @@ from .file_utils import (
     requires_datasets,
     requires_faiss,
 )
+from ...tokenization_utils_base import BatchEncoding
+from ...utils import logging
+from .configuration_rag import RagConfig
 from .tokenization_rag import RagTokenizer
-from .tokenization_utils_base import BatchEncoding
-from .utils import logging
 
 
 if is_datasets_available():
@@ -105,7 +105,7 @@ class LegacyIndex(Index):
             The dimension of indexed vectors.
         index_path (:obj:`str`):
             A path to a `directory` containing index files compatible with
-            :class:`~transformers.retrieval_rag.LegacyIndex`
+            :class:`~transformers.models.rag.retrieval_rag.LegacyIndex`
     """
 
     INDEX_FILENAME = "hf_bert_base.hnswSQ8_correct_phi_128.c_index"
@@ -344,7 +344,7 @@ class RagRetriever:
             generator_tokenizer.
         generator_tokenizer (:class:`~transformers.PreTrainedTokenizer`):
             The tokenizer used for the generator part of the RagModel.
-        index (:class:`~transformers.retrieval_rag.Index`, optional, defaults to the one defined by the configuration):
+        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::

src/transformers/tokenization_rag.py → src/transformers/models/rag/tokenization_rag.py

@@ -16,10 +16,10 @@
 import os
 from typing import List, Optional
 
+from ...file_utils import add_start_docstrings
+from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, BatchEncoding
+from ...utils import logging
 from .configuration_rag import RagConfig
-from .file_utils import add_start_docstrings
-from .tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, BatchEncoding
-from .utils import logging
 
 
 logger = logging.get_logger(__name__)
@@ -42,7 +42,7 @@ class RagTokenizer:
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
         # dynamically import AutoTokenizer
-        from .tokenization_auto import AutoTokenizer
+        from ..auto.tokenization_auto import AutoTokenizer
 
         config = kwargs.pop("config", None)
 

src/transformers/models/reformer/__init__.py

+# flake8: noqa
+# There's no way to ignore "F401 '...' imported but unused" warnings in this
+# module, but to preserve other warnings. So, don't check this module at all.
+
+from ...file_utils import is_sentencepiece_available, is_tokenizers_available, is_torch_available
+from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
+
+
+if is_sentencepiece_available():
+    from .tokenization_reformer import ReformerTokenizer
+
+if is_tokenizers_available():
+    from .tokenization_reformer_fast import ReformerTokenizerFast
+
+if is_torch_available():
+    from .modeling_reformer import (
+        REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
+        ReformerAttention,
+        ReformerForMaskedLM,
+        ReformerForQuestionAnswering,
+        ReformerForSequenceClassification,
+        ReformerLayer,
+        ReformerModel,
+        ReformerModelWithLMHead,
+    )

src/transformers/configuration_reformer.py → src/transformers/models/reformer/configuration_reformer.py

@@ -15,8 +15,8 @@
 # limitations under the License.
 """ Reformer model configuration """
 
-from .configuration_utils import PretrainedConfig
-from .utils import logging
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
 
 
 logger = logging.get_logger(__name__)

src/transformers/modeling_reformer.py → src/transformers/models/reformer/modeling_reformer.py

@@ -28,9 +28,8 @@ from torch import nn
 from torch.autograd.function import Function
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .activations import ACT2FN
-from .configuration_reformer import ReformerConfig
-from .file_utils import (
+from ...activations import ACT2FN
+from ...file_utils import (
     DUMMY_INPUTS,
     DUMMY_MASK,
     ModelOutput,
@@ -38,9 +37,10 @@ from .file_utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
 )
-from .modeling_outputs import CausalLMOutput, MaskedLMOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput
-from .modeling_utils import PreTrainedModel, apply_chunking_to_forward
-from .utils import logging
+from ...modeling_outputs import CausalLMOutput, MaskedLMOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput
+from ...modeling_utils import PreTrainedModel, apply_chunking_to_forward
+from ...utils import logging
+from .configuration_reformer import ReformerConfig
 
 
 logger = logging.get_logger(__name__)

src/transformers/tokenization_reformer.py → src/transformers/models/reformer/tokenization_reformer.py

@@ -21,8 +21,8 @@ from typing import Dict, Optional, Tuple
 
 import sentencepiece as spm
 
-from .tokenization_utils import PreTrainedTokenizer
-from .utils import logging
+from ...tokenization_utils import PreTrainedTokenizer
+from ...utils import logging
 
 
 logger = logging.get_logger(__name__)

src/transformers/tokenization_reformer_fast.py → src/transformers/models/reformer/tokenization_reformer_fast.py

@@ -19,9 +19,9 @@ import os
 from shutil import copyfile
 from typing import Optional, Tuple
 
-from .file_utils import is_sentencepiece_available
-from .tokenization_utils_fast import PreTrainedTokenizerFast
-from .utils import logging
+from ...file_utils import is_sentencepiece_available
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import logging
 
 
 if is_sentencepiece_available():

src/transformers/models/retribert/__init__.py

+# flake8: noqa
+# There's no way to ignore "F401 '...' imported but unused" warnings in this
+# module, but to preserve other warnings. So, don't check this module at all.
+
+from ...file_utils import is_tokenizers_available, is_torch_available
+from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
+from .tokenization_retribert import RetriBertTokenizer
+
+
+if is_tokenizers_available():
+    from .tokenization_retribert_fast import RetriBertTokenizerFast
+
+if is_torch_available():
+    from .modeling_retribert import RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST, RetriBertModel, RetriBertPreTrainedModel

src/transformers/configuration_retribert.py → src/transformers/models/retribert/configuration_retribert.py

@@ -14,8 +14,8 @@
 # limitations under the License.
 """ RetriBERT model configuration """
 
-from .configuration_utils import PretrainedConfig
-from .utils import logging
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
 
 
 logger = logging.get_logger(__name__)

src/transformers/modeling_retribert.py → src/transformers/models/retribert/modeling_retribert.py

@@ -23,11 +23,11 @@ import torch
 import torch.nn as nn
 import torch.utils.checkpoint as checkpoint
 
+from ...file_utils import add_start_docstrings
+from ...modeling_utils import PreTrainedModel
+from ...utils import logging
+from ..bert.modeling_bert import BertModel
 from .configuration_retribert import RetriBertConfig
-from .file_utils import add_start_docstrings
-from .modeling_bert import BertModel
-from .modeling_utils import PreTrainedModel
-from .utils import logging
 
 
 logger = logging.get_logger(__name__)

src/transformers/tokenization_retribert.py → src/transformers/models/retribert/tokenization_retribert.py

@@ -14,8 +14,8 @@
 # limitations under the License.
 """Tokenization classes for RetriBERT."""
 
-from .tokenization_bert import BertTokenizer
-from .utils import logging
+from ...utils import logging
+from ..bert.tokenization_bert import BertTokenizer
 
 
 logger = logging.get_logger(__name__)

src/transformers/tokenization_retribert_fast.py → src/transformers/models/retribert/tokenization_retribert_fast.py

@@ -14,9 +14,9 @@
 # limitations under the License.
 """Tokenization classes for RetriBERT."""
 
-from .tokenization_bert_fast import BertTokenizerFast
+from ...utils import logging
+from ..bert.tokenization_bert_fast import BertTokenizerFast
 from .tokenization_retribert import RetriBertTokenizer
-from .utils import logging
 
 
 logger = logging.get_logger(__name__)

src/transformers/models/roberta/__init__.py

+# flake8: noqa
+# There's no way to ignore "F401 '...' imported but unused" warnings in this
+# module, but to preserve other warnings. So, don't check this module at all.
+
+from ...file_utils import is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
+from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
+from .tokenization_roberta import RobertaTokenizer
+
+
+if is_tokenizers_available():
+    from .tokenization_roberta_fast import RobertaTokenizerFast
+
+if is_torch_available():
+    from .modeling_roberta import (
+        ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
+        RobertaForCausalLM,
+        RobertaForMaskedLM,
+        RobertaForMultipleChoice,
+        RobertaForQuestionAnswering,
+        RobertaForSequenceClassification,
+        RobertaForTokenClassification,
+        RobertaModel,
+    )
+
+if is_tf_available():
+    from .modeling_tf_roberta import (
+        TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
+        TFRobertaForMaskedLM,
+        TFRobertaForMultipleChoice,
+        TFRobertaForQuestionAnswering,
+        TFRobertaForSequenceClassification,
+        TFRobertaForTokenClassification,
+        TFRobertaMainLayer,
+        TFRobertaModel,
+        TFRobertaPreTrainedModel,
+    )
+
+if is_flax_available():
+    from .modeling_flax_roberta import FlaxRobertaModel

src/transformers/configuration_roberta.py → src/transformers/models/roberta/configuration_roberta.py

@@ -15,8 +15,8 @@
 # limitations under the License.
 """ RoBERTa configuration """
 
-from .configuration_bert import BertConfig
-from .utils import logging
+from ...utils import logging
+from ..bert.configuration_bert import BertConfig
 
 
 logger = logging.get_logger(__name__)

src/transformers/convert_roberta_original_pytorch_checkpoint_to_pytorch.py → src/transformers/models/roberta/convert_roberta_original_pytorch_checkpoint_to_pytorch.py

@@ -24,8 +24,18 @@ from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
 from fairseq.modules import TransformerSentenceEncoderLayer
 from packaging import version
 
-from transformers.modeling_bert import BertIntermediate, BertLayer, BertOutput, BertSelfAttention, BertSelfOutput
-from transformers.modeling_roberta import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
+from transformers.models.bertmodeling_bert import (
+    BertIntermediate,
+    BertLayer,
+    BertOutput,
+    BertSelfAttention,
+    BertSelfOutput,
+)
+from transformers.models.roberta.modeling_roberta import (
+    RobertaConfig,
+    RobertaForMaskedLM,
+    RobertaForSequenceClassification,
+)
 from transformers.utils import logging
 
 

src/transformers/modeling_flax_roberta.py → src/transformers/models/roberta/modeling_flax_roberta.py

@@ -20,10 +20,10 @@ import flax.linen as nn
 import jax
 import jax.numpy as jnp
 
+from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
+from ...modeling_flax_utils import FlaxPreTrainedModel, gelu
+from ...utils import logging
 from .configuration_roberta import RobertaConfig
-from .file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
-from .modeling_flax_utils import FlaxPreTrainedModel, gelu
-from .utils import logging
 
 
 logger = logging.get_logger(__name__)
@@ -89,7 +89,7 @@ ROBERTA_INPUTS_DOCSTRING = r"""
 """
 
 
-# Copied from transformers.modeling_flax_bert.FlaxBertLayerNorm with Bert->Roberta
+# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayerNorm with Bert->Roberta
 class FlaxRobertaLayerNorm(nn.Module):
     """
     Layer normalization (https://arxiv.org/abs/1607.06450). Operates on the last axis of the input data.
@@ -130,7 +130,7 @@ class FlaxRobertaLayerNorm(nn.Module):
         return y
 
 
-# Copied from transformers.modeling_flax_bert.FlaxBertEmbedding with Bert->Roberta
+# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbedding with Bert->Roberta
 class FlaxRobertaEmbedding(nn.Module):
     """
     Specify a new class for doing the embedding stuff as Flax's one use 'embedding' for the parameter name and PyTorch
@@ -147,7 +147,7 @@ class FlaxRobertaEmbedding(nn.Module):
         return jnp.take(embedding, inputs, axis=0)
 
 
-# Copied from transformers.modeling_flax_bert.FlaxBertEmbeddings with Bert->Roberta
+# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings with Bert->Roberta
 class FlaxRobertaEmbeddings(nn.Module):
     """Construct the embeddings from word, position and token_type embeddings."""
 
@@ -179,7 +179,7 @@ class FlaxRobertaEmbeddings(nn.Module):
         return layer_norm
 
 
-# Copied from transformers.modeling_flax_bert.FlaxBertAttention with Bert->Roberta
+# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertAttention with Bert->Roberta
 class FlaxRobertaAttention(nn.Module):
     num_heads: int
     head_size: int
@@ -194,7 +194,7 @@ class FlaxRobertaAttention(nn.Module):
         return layer_norm
 
 
-# Copied from transformers.modeling_flax_bert.FlaxBertIntermediate with Bert->Roberta
+# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->Roberta
 class FlaxRobertaIntermediate(nn.Module):
     output_size: int
 
@@ -205,7 +205,7 @@ class FlaxRobertaIntermediate(nn.Module):
         return gelu(dense)
 
 
-# Copied from transformers.modeling_flax_bert.FlaxBertOutput with Bert->Roberta
+# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->Roberta
 class FlaxRobertaOutput(nn.Module):
     @nn.compact
     def __call__(self, intermediate_output, attention_output):
@@ -230,7 +230,7 @@ class FlaxRobertaLayer(nn.Module):
         return output
 
 
-# Copied from transformers.modeling_flax_bert.FlaxBertLayerCollection with Bert->Roberta
+# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayerCollection with Bert->Roberta
 class FlaxRobertaLayerCollection(nn.Module):
     """
     Stores N RobertaLayer(s)
@@ -255,7 +255,7 @@ class FlaxRobertaLayerCollection(nn.Module):
         return input_i
 
 
-# Copied from transformers.modeling_flax_bert.FlaxBertEncoder with Bert->Roberta
+# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEncoder with Bert->Roberta
 class FlaxRobertaEncoder(nn.Module):
     num_layers: int
     num_heads: int
@@ -270,7 +270,7 @@ class FlaxRobertaEncoder(nn.Module):
         return layer
 
 
-# Copied from transformers.modeling_flax_bert.FlaxBertPooler with Bert->Roberta
+# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPooler with Bert->Roberta
 class FlaxRobertaPooler(nn.Module):
     @nn.compact
     def __call__(self, hidden_state):
@@ -279,7 +279,7 @@ class FlaxRobertaPooler(nn.Module):
         return jax.lax.tanh(out)
 
 
-# Copied from transformers.modeling_flax_bert.FlaxBertModule with Bert->Roberta
+# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModule with Bert->Roberta
 class FlaxRobertaModule(nn.Module):
     vocab_size: int
     hidden_size: int

src/transformers/modeling_roberta.py → src/transformers/models/roberta/modeling_roberta.py

@@ -22,15 +22,14 @@ import torch
 import torch.nn as nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .activations import ACT2FN, gelu
-from .configuration_roberta import RobertaConfig
-from .file_utils import (
+from ...activations import ACT2FN, gelu
+from ...file_utils import (
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
-from .modeling_outputs import (
+from ...modeling_outputs import (
     BaseModelOutputWithCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
     CausalLMOutputWithCrossAttentions,
@@ -40,13 +39,14 @@ from .modeling_outputs import (
     SequenceClassifierOutput,
     TokenClassifierOutput,
 )
-from .modeling_utils import (
+from ...modeling_utils import (
     PreTrainedModel,
     apply_chunking_to_forward,
     find_pruneable_heads_and_indices,
     prune_linear_layer,
 )
-from .utils import logging
+from ...utils import logging
+from .configuration_roberta import RobertaConfig
 
 
 logger = logging.get_logger(__name__)
@@ -70,7 +70,7 @@ class RobertaEmbeddings(nn.Module):
     Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
     """
 
-    # Copied from transformers.modeling_bert.BertEmbeddings.__init__
+    # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
     def __init__(self, config):
         super().__init__()
         self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
@@ -99,7 +99,7 @@ class RobertaEmbeddings(nn.Module):
             else:
                 position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
 
-        # Copied from transformers.modeling_bert.BertEmbeddings.forward
+        # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.forward
         if input_ids is not None:
             input_shape = input_ids.size()
         else:
@@ -141,7 +141,7 @@ class RobertaEmbeddings(nn.Module):
         return position_ids.unsqueeze(0).expand(input_shape)
 
 
-# Copied from transformers.modeling_bert.BertSelfAttention with Bert->Roberta
+# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta
 class RobertaSelfAttention(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -220,7 +220,7 @@ class RobertaSelfAttention(nn.Module):
         return outputs
 
 
-# Copied from transformers.modeling_bert.BertSelfOutput
+# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
 class RobertaSelfOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -235,7 +235,7 @@ class RobertaSelfOutput(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.modeling_bert.BertAttention with Bert->Roberta
+# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta
 class RobertaAttention(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -283,7 +283,7 @@ class RobertaAttention(nn.Module):
         return outputs
 
 
-# Copied from transformers.modeling_bert.BertIntermediate
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate
 class RobertaIntermediate(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -299,7 +299,7 @@ class RobertaIntermediate(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.modeling_bert.BertOutput
+# Copied from transformers.models.bert.modeling_bert.BertOutput
 class RobertaOutput(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -314,7 +314,7 @@ class RobertaOutput(nn.Module):
         return hidden_states
 
 
-# Copied from transformers.modeling_bert.BertLayer with Bert->Roberta
+# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta
 class RobertaLayer(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -374,7 +374,7 @@ class RobertaLayer(nn.Module):
         return layer_output
 
 
-# Copied from transformers.modeling_bert.BertEncoder with Bert->Roberta
+# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta
 class RobertaEncoder(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -449,7 +449,7 @@ class RobertaEncoder(nn.Module):
         )
 
 
-# Copied from transformers.modeling_bert.BertPooler
+# Copied from transformers.models.bert.modeling_bert.BertPooler
 class RobertaPooler(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -474,7 +474,7 @@ class RobertaPreTrainedModel(PreTrainedModel):
     config_class = RobertaConfig
     base_model_prefix = "roberta"
 
-    # Copied from transformers.modeling_bert.BertPreTrainedModel._init_weights
+    # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
     def _init_weights(self, module):
         """ Initialize the weights """
         if isinstance(module, (nn.Linear, nn.Embedding)):
@@ -579,7 +579,7 @@ class RobertaModel(RobertaPreTrainedModel):
 
     authorized_missing_keys = [r"position_ids"]
 
-    # Copied from transformers.modeling_bert.BertModel.__init__ with Bert->Roberta
+    # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta
     def __init__(self, config, add_pooling_layer=True):
         super().__init__(config)
         self.config = config
@@ -612,7 +612,7 @@ class RobertaModel(RobertaPreTrainedModel):
         output_type=BaseModelOutputWithPoolingAndCrossAttentions,
         config_class=_CONFIG_FOR_DOC,
     )
-    # Copied from transformers.modeling_bert.BertModel.forward
+    # Copied from transformers.models.bert.modeling_bert.BertModel.forward
     def forward(
         self,
         input_ids=None,

src/transformers/modeling_tf_roberta.py → src/transformers/models/roberta/modeling_tf_roberta.py

@@ -18,15 +18,14 @@
 
 import tensorflow as tf
 
-from .activations_tf import get_tf_activation
-from .configuration_roberta import RobertaConfig
-from .file_utils import (
+from ...activations_tf import get_tf_activation
+from ...file_utils import (
     MULTIPLE_CHOICE_DUMMY_INPUTS,
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
 )
-from .modeling_tf_outputs import (
+from ...modeling_tf_outputs import (
     TFBaseModelOutput,
     TFBaseModelOutputWithPooling,
     TFMaskedLMOutput,
@@ -35,7 +34,7 @@ from .modeling_tf_outputs import (
     TFSequenceClassifierOutput,
     TFTokenClassifierOutput,
 )
-from .modeling_tf_utils import (
+from ...modeling_tf_utils import (
     TFMaskedLanguageModelingLoss,
     TFMultipleChoiceLoss,
     TFPreTrainedModel,
@@ -46,8 +45,9 @@ from .modeling_tf_utils import (
     keras_serializable,
     shape_list,
 )
-from .tokenization_utils_base import BatchEncoding
-from .utils import logging
+from ...tokenization_utils_base import BatchEncoding
+from ...utils import logging
+from .configuration_roberta import RobertaConfig
 
 
 logger = logging.get_logger(__name__)
@@ -223,7 +223,7 @@ class TFRobertaEmbeddings(tf.keras.layers.Layer):
         return tf.reshape(logits, [batch_size, length, self.vocab_size])
 
 
-# Copied from transformers.modeling_tf_bert.TFBertPooler
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler
 class TFRobertaPooler(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
@@ -244,7 +244,7 @@ class TFRobertaPooler(tf.keras.layers.Layer):
         return pooled_output
 
 
-# Copied from transformers.modeling_tf_bert.TFBertSelfAttention
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention
 class TFRobertaSelfAttention(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
@@ -316,7 +316,7 @@ class TFRobertaSelfAttention(tf.keras.layers.Layer):
         return outputs
 
 
-# Copied from transformers.modeling_tf_bert.TFBertSelfOutput
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput
 class TFRobertaSelfOutput(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
@@ -335,7 +335,7 @@ class TFRobertaSelfOutput(tf.keras.layers.Layer):
         return hidden_states
 
 
-# Copied from transformers.modeling_tf_bert.TFBertAttention with Bert->Roberta
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->Roberta
 class TFRobertaAttention(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
@@ -356,7 +356,7 @@ class TFRobertaAttention(tf.keras.layers.Layer):
         return outputs
 
 
-# Copied from transformers.modeling_tf_bert.TFBertIntermediate
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate
 class TFRobertaIntermediate(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
@@ -377,7 +377,7 @@ class TFRobertaIntermediate(tf.keras.layers.Layer):
         return hidden_states
 
 
-# Copied from transformers.modeling_tf_bert.TFBertOutput
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput
 class TFRobertaOutput(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
@@ -396,7 +396,7 @@ class TFRobertaOutput(tf.keras.layers.Layer):
         return hidden_states
 
 
-# Copied from transformers.modeling_tf_bert.TFBertLayer with Bert->Roberta
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->Roberta
 class TFRobertaLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
@@ -417,7 +417,7 @@ class TFRobertaLayer(tf.keras.layers.Layer):
         return outputs
 
 
-# Copied from transformers.modeling_tf_bert.TFBertEncoder with Bert->Roberta
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->Roberta
 class TFRobertaEncoder(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
@@ -478,16 +478,16 @@ class TFRobertaMainLayer(tf.keras.layers.Layer):
         # The embeddings must be the last declaration in order to follow the weights order
         self.embeddings = TFRobertaEmbeddings(config, name="embeddings")
 
-    # Copied from transformers.modeling_tf_bert.TFBertMainLayer.get_input_embeddings
+    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.get_input_embeddings
     def get_input_embeddings(self):
         return self.embeddings
 
-    # Copied from transformers.modeling_tf_bert.TFBertMainLayer.set_input_embeddings
+    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.set_input_embeddings
     def set_input_embeddings(self, value):
         self.embeddings.word_embeddings = value
         self.embeddings.vocab_size = value.shape[0]
 
-    # Copied from transformers.modeling_tf_bert.TFBertMainLayer._prune_heads
+    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer._prune_heads
     def _prune_heads(self, heads_to_prune):
         """
         Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
@@ -495,7 +495,7 @@ class TFRobertaMainLayer(tf.keras.layers.Layer):
         """
         raise NotImplementedError
 
-    # Copied from transformers.modeling_tf_bert.TFBertMainLayer.call
+    # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.call
     def call(
         self,
         inputs,

src/transformers/tokenization_roberta.py → src/transformers/models/roberta/tokenization_roberta.py

@@ -17,9 +17,9 @@
 import warnings
 from typing import List, Optional
 
-from .tokenization_gpt2 import GPT2Tokenizer
-from .tokenization_utils import AddedToken
-from .utils import logging
+from ...tokenization_utils import AddedToken
+from ...utils import logging
+from ..gpt2.tokenization_gpt2 import GPT2Tokenizer
 
 
 logger = logging.get_logger(__name__)