From 7d9d5cea551b7ac5a6222c0b32a4c269412f7b9a Mon Sep 17 00:00:00 2001
From: "Hz, Ji" <hzji210@gmail.com>
Date: Mon, 8 Jan 2024 19:33:58 +0800
Subject: [PATCH] remove two deprecated function (#28220)

---
 src/transformers/utils/__init__.py     |  2 --
 src/transformers/utils/import_utils.py | 36 +-------------------------
 2 files changed, 1 insertion(+), 37 deletions(-)

diff --git a/src/transformers/utils/__init__.py b/src/transformers/utils/__init__.py
index ff2723473f1..780090aec5e 100644
--- a/src/transformers/utils/__init__.py
+++ b/src/transformers/utils/__init__.py
@@ -194,9 +194,7 @@
     is_training_run_on_sagemaker,
     is_vision_available,
     requires_backends,
-    tf_required,
     torch_only_method,
-    torch_required,
 )
 from .peft_utils import (
     ADAPTER_CONFIG_NAME,
diff --git a/src/transformers/utils/import_utils.py b/src/transformers/utils/import_utils.py
index eda34597a39..909965d0306 100644
--- a/src/transformers/utils/import_utils.py
+++ b/src/transformers/utils/import_utils.py
@@ -24,7 +24,7 @@
 import sys
 import warnings
 from collections import OrderedDict
-from functools import lru_cache, wraps
+from functools import lru_cache
 from itertools import chain
 from types import ModuleType
 from typing import Any, Tuple, Union
@@ -1303,40 +1303,6 @@ def __getattribute__(cls, key):
         requires_backends(cls, cls._backends)
 
 
-def torch_required(func):
-    warnings.warn(
-        "The method `torch_required` is deprecated and will be removed in v4.36. Use `requires_backends` instead.",
-        FutureWarning,
-    )
-
-    # Chose a different decorator name than in tests so it's clear they are not the same.
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        if is_torch_available():
-            return func(*args, **kwargs)
-        else:
-            raise ImportError(f"Method `{func.__name__}` requires PyTorch.")
-
-    return wrapper
-
-
-def tf_required(func):
-    warnings.warn(
-        "The method `tf_required` is deprecated and will be removed in v4.36. Use `requires_backends` instead.",
-        FutureWarning,
-    )
-
-    # Chose a different decorator name than in tests so it's clear they are not the same.
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        if is_tf_available():
-            return func(*args, **kwargs)
-        else:
-            raise ImportError(f"Method `{func.__name__}` requires TF.")
-
-    return wrapper
-
-
 def is_torch_fx_proxy(x):
     if is_torch_fx_available():
         import torch.fx