TF/Numpy variants for all DataCollator classes (#13105)

* Adding a TF variant of the DataCollatorForTokenClassification to get feedback

* Added a Numpy variant and a post_init check to fail early if a missing import is found

* Fixed call to Numpy variant

* Added a couple more of the collators

* Update src/transformers/data/data_collator.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Fixes, style pass, finished DataCollatorForSeqToSeq

* Added all the LanguageModeling DataCollators, except SOP and PermutationLanguageModeling

* Adding DataCollatorForPermutationLanguageModeling

* Style pass

* Add missing `__call__` for PLM

* Remove `post_init` checks for frameworks because the imports inside them were making us fail code quality checks

* Remove unused imports

* First attempt at some TF tests

* A second attempt to make any of those tests actually work

* TF tests, round three

* TF tests, round four

* TF tests, round five

* TF tests, all enabled!

* Style pass

* Merging tests into `test_data_collator.py`

* Merging tests into `test_data_collator.py`

* Fixing up test imports

* Fixing up test imports

* Trying shuffling the conditionals around

* Commenting out non-functional old tests

* Completed all tests for all three frameworks

* Style pass

* Fixed test typo

* Style pass

* Move standard `__call__` method to mixin

* Rearranged imports for `test_data_collator`

* Fix data collator typo "torch" -> "pt"

* Fixed the most embarrassingly obvious bug

* Update src/transformers/data/data_collator.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Renaming mixin

* Updating docs
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Dalton Walker <dalton_walker@icloud.com>
Co-authored-by: Andrew Romans <andrew.romans@hotmail.com>

docs/source/main_classes/data_collator.rst

@@ -54,18 +54,18 @@ DataCollatorForLanguageModeling
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.data.data_collator.DataCollatorForLanguageModeling
-    :members: mask_tokens
+    :members: numpy_mask_tokens, tf_mask_tokens, torch_mask_tokens
 
 
 DataCollatorForWholeWordMask
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.data.data_collator.DataCollatorForWholeWordMask
-    :members: mask_tokens
+    :members: numpy_mask_tokens, tf_mask_tokens, torch_mask_tokens
 
 
 DataCollatorForPermutationLanguageModeling
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.data.data_collator.DataCollatorForPermutationLanguageModeling
-    :members: mask_tokens
+    :members: numpy_mask_tokens, tf_mask_tokens, torch_mask_tokens

src/transformers/__init__.py

@@ -81,6 +81,17 @@ _import_structure = {
         "xnli_processors",
         "xnli_tasks_num_labels",
     ],
+    "data.data_collator": [
+        "DataCollator",
+        "DataCollatorForLanguageModeling",
+        "DataCollatorForPermutationLanguageModeling",
+        "DataCollatorForSeq2Seq",
+        "DataCollatorForSOP",
+        "DataCollatorForTokenClassification",
+        "DataCollatorForWholeWordMask",
+        "DataCollatorWithPadding",
+        "default_data_collator",
+    ],
     "feature_extraction_sequence_utils": ["BatchFeature", "SequenceFeatureExtractor"],
     "file_utils": [
         "CONFIG_NAME",
@@ -460,17 +471,6 @@ else:
 if is_torch_available():
     _import_structure["benchmark.benchmark"] = ["PyTorchBenchmark"]
     _import_structure["benchmark.benchmark_args"] = ["PyTorchBenchmarkArguments"]
-    _import_structure["data.data_collator"] = [
-        "DataCollator",
-        "DataCollatorForLanguageModeling",
-        "DataCollatorForPermutationLanguageModeling",
-        "DataCollatorForSeq2Seq",
-        "DataCollatorForSOP",
-        "DataCollatorForTokenClassification",
-        "DataCollatorForWholeWordMask",
-        "DataCollatorWithPadding",
-        "default_data_collator",
-    ]
     _import_structure["data.datasets"] = [
         "GlueDataset",
         "GlueDataTrainingArguments",
@@ -1830,6 +1830,17 @@ if TYPE_CHECKING:
         xnli_processors,
         xnli_tasks_num_labels,
     )
+    from .data.data_collator import (
+        DataCollator,
+        DataCollatorForLanguageModeling,
+        DataCollatorForPermutationLanguageModeling,
+        DataCollatorForSeq2Seq,
+        DataCollatorForSOP,
+        DataCollatorForTokenClassification,
+        DataCollatorForWholeWordMask,
+        DataCollatorWithPadding,
+        default_data_collator,
+    )
 
     # Feature Extractor
     from .feature_extraction_utils import BatchFeature, SequenceFeatureExtractor
@@ -2174,17 +2185,6 @@ if TYPE_CHECKING:
         # Benchmarks
         from .benchmark.benchmark import PyTorchBenchmark
         from .benchmark.benchmark_args import PyTorchBenchmarkArguments
-        from .data.data_collator import (
-            DataCollator,
-            DataCollatorForLanguageModeling,
-            DataCollatorForPermutationLanguageModeling,
-            DataCollatorForSeq2Seq,
-            DataCollatorForSOP,
-            DataCollatorForTokenClassification,
-            DataCollatorForWholeWordMask,
-            DataCollatorWithPadding,
-            default_data_collator,
-        )
         from .data.datasets import (
             GlueDataset,
             GlueDataTrainingArguments,

src/transformers/utils/dummy_pt_objects.py

@@ -12,62 +12,6 @@ class PyTorchBenchmarkArguments:
         requires_backends(self, ["torch"])
 
 
-class DataCollator:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-
-class DataCollatorForLanguageModeling:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
-class DataCollatorForPermutationLanguageModeling:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
-class DataCollatorForSeq2Seq:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-
-class DataCollatorForSOP:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-
-class DataCollatorForTokenClassification:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
-class DataCollatorForWholeWordMask:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-
-class DataCollatorWithPadding:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-
-def default_data_collator(*args, **kwargs):
-    requires_backends(default_data_collator, ["torch"])
-
-
 class GlueDataset:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])