Fast imports part 3 (#9474)

* New intermediate inits

* Update template

* Avoid importing torch/tf/flax in tokenization unless necessary

* Styling

* Shutup flake8

* Better python version check

src/transformers/tokenization_utils_base.py

@@ -25,7 +25,7 @@ import warnings
 from collections import OrderedDict, UserDict
 from dataclasses import dataclass, field
 from enum import Enum
-from typing import Any, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union
+from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union
 
 import numpy as np
 
@@ -45,21 +45,34 @@ from .file_utils import (
 from .utils import logging
 
 
-if is_tf_available():
-    import tensorflow as tf
-
-if is_torch_available():
-    import torch
-
-if is_flax_available():
-    import jax.numpy as jnp
+if TYPE_CHECKING:
+    if is_torch_available():
+        import torch
+    if is_tf_available():
+        import tensorflow as tf
+    if is_flax_available():
+        import jax.numpy as jnp  # noqa: F401
 
 
 def _is_numpy(x):
     return isinstance(x, np.ndarray)
 
 
+def _is_torch(x):
+    import torch
+
+    return isinstance(x, torch.Tensor)
+
+
+def _is_tensorflow(x):
+    import tensorflow as tf
+
+    return isinstance(x, tf.Tensor)
+
+
 def _is_jax(x):
+    import jax.numpy as jnp  # noqa: F811
+
     return isinstance(x, jnp.ndarray)
 
 
@@ -196,9 +209,9 @@ def to_py_obj(obj):
         return {k: to_py_obj(v) for k, v in obj.items()}
     elif isinstance(obj, (list, tuple)):
         return [to_py_obj(o) for o in obj]
-    elif is_tf_available() and isinstance(obj, tf.Tensor):
+    elif is_tf_available() and _is_tensorflow(obj):
         return obj.numpy().tolist()
-    elif is_torch_available() and isinstance(obj, torch.Tensor):
+    elif is_torch_available() and _is_torch(obj):
         return obj.detach().cpu().tolist()
     elif isinstance(obj, np.ndarray):
         return obj.tolist()
@@ -714,16 +727,22 @@ class BatchEncoding(UserDict):
                 raise ImportError(
                     "Unable to convert output to TensorFlow tensors format, TensorFlow is not installed."
                 )
+            import tensorflow as tf
+
             as_tensor = tf.constant
             is_tensor = tf.is_tensor
         elif tensor_type == TensorType.PYTORCH:
             if not is_torch_available():
                 raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.")
+            import torch
+
             as_tensor = torch.tensor
             is_tensor = torch.is_tensor
         elif tensor_type == TensorType.JAX:
             if not is_flax_available():
                 raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed.")
+            import jax.numpy as jnp  # noqa: F811
+
             as_tensor = jnp.array
             is_tensor = _is_jax
         else:
@@ -2684,9 +2703,9 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
                 first_element = encoded_inputs["input_ids"][index][0]
         # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do.
         if not isinstance(first_element, (int, list, tuple)):
-            if is_tf_available() and isinstance(first_element, tf.Tensor):
+            if is_tf_available() and _is_tensorflow(first_element):
                 return_tensors = "tf" if return_tensors is None else return_tensors
-            elif is_torch_available() and isinstance(first_element, torch.Tensor):
+            elif is_torch_available() and _is_torch(first_element):
                 return_tensors = "pt" if return_tensors is None else return_tensors
             elif isinstance(first_element, np.ndarray):
                 return_tensors = "np" if return_tensors is None else return_tensors