Transformers fast import part 2 (#9446)

* Main init work

* Add version

* Change from absolute to relative imports

* Fix imports

* One more typo

* More typos

* Styling

* Make quality script pass

* Add necessary replace in template

* Fix typos

* Spaces are ignored in replace for some reason

* Forgot one models.

* Fixes for import

Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>

* Add documentation

* Styling

Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>

src/transformers/file_utils.py

@@ -33,6 +33,7 @@ from dataclasses import fields
 from functools import partial, wraps
 from hashlib import sha256
 from pathlib import Path
+from types import ModuleType
 from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
 from urllib.parse import urlparse
 from zipfile import ZipFile, is_zipfile
@@ -41,7 +42,6 @@ import numpy as np
 from packaging import version
 from tqdm.auto import tqdm
 
-import importlib_metadata
 import requests
 from filelock import FileLock
 
@@ -50,6 +50,13 @@ from .hf_api import HfFolder
 from .utils import logging
 
 
+# The package importlib_metadata is in a different place, depending on the python version.
+if version.parse(sys.version) < version.parse("3.8"):
+    import importlib_metadata
+else:
+    import importlib.metadata as importlib_metadata
+
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 ENV_VARS_TRUE_VALUES = {"1", "ON", "YES"}
@@ -130,7 +137,7 @@ except importlib_metadata.PackageNotFoundError:
 
 _scatter_available = importlib.util.find_spec("torch_scatter") is not None
 try:
-    _scatter_version = importlib_metadata.version("torch_scatterr")
+    _scatter_version = importlib_metadata.version("torch_scatter")
     logger.debug(f"Successfully imported torch-scatter version {_scatter_version}")
 except importlib_metadata.PackageNotFoundError:
     _scatter_available = False
@@ -1415,3 +1422,40 @@ class ModelOutput(OrderedDict):
         Convert self to a tuple containing all the attributes/keys that are not ``None``.
         """
         return tuple(self[k] for k in self.keys())
+
+
+class _BaseLazyModule(ModuleType):
+    """
+    Module class that surfaces all objects but only performs associated imports when the objects are requested.
+    """
+
+    # Very heavily inspired by optuna.integration._IntegrationModule
+    # https://github.com/optuna/optuna/blob/master/optuna/integration/__init__.py
+    def __init__(self, name, import_structure):
+        super().__init__(name)
+        self._modules = set(import_structure.keys())
+        self._class_to_module = {}
+        for key, values in import_structure.items():
+            for value in values:
+                self._class_to_module[value] = key
+        # Needed for autocompletion in an IDE
+        self.__all__ = list(import_structure.keys()) + sum(import_structure.values(), [])
+
+    # Needed for autocompletion in an IDE
+    def __dir__(self):
+        return super().__dir__() + self.__all__
+
+    def __getattr__(self, name: str) -> Any:
+        if name in self._modules:
+            value = self._get_module(name)
+        elif name in self._class_to_module.keys():
+            module = self._get_module(self._class_to_module[name])
+            value = getattr(module, name)
+        else:
+            raise AttributeError(f"module {self.__name__} has no attribute {name}")
+
+        setattr(self, name, value)
+        return value
+
+    def _get_module(self, module_name: str) -> ModuleType:
+        raise NotImplementedError