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+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
+
+import warnings
+
+from .state import AcceleratorState, GradientState
+
+
+warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
+
+
+class AcceleratedScheduler:
+    """
+    A wrapper around a learning rate scheduler that will only step when the optimizer(s) have a training step. Useful
+    to avoid making a scheduler step too fast when gradients went overflow and there was no training step (in mixed
+    precision training)
+
+    When performing gradient accumulation scheduler lengths should not be changed accordingly, Accelerate will always
+    step the scheduler to account for it.
+
+    Args:
+        scheduler (`torch.optim.lr_scheduler._LRScheduler`):
+            The scheduler to wrap.
+        optimizers (one or a list of `torch.optim.Optimizer`):
+            The optimizers used.
+        step_with_optimizer (`bool`, *optional*, defaults to `True`):
+            Whether or not the scheduler should be stepped at each optimizer step.
+        split_batches (`bool`, *optional*, defaults to `False`):
+            Whether or not the dataloaders split one batch across the different processes (so batch size is the same
+            regardless of the number of processes) or create batches on each process (so batch size is the original
+            batch size multiplied by the number of processes).
+    """
+
+    def __init__(self, scheduler, optimizers, step_with_optimizer: bool = True, split_batches: bool = False):
+        self.scheduler = scheduler
+        self.optimizers = optimizers if isinstance(optimizers, (list, tuple)) else [optimizers]
+        self.split_batches = split_batches
+        self.step_with_optimizer = step_with_optimizer
+        self.gradient_state = GradientState()
+
+    def step(self, *args, **kwargs):
+        if not self.step_with_optimizer:
+            # No link between scheduler and optimizer -> just step
+            self.scheduler.step(*args, **kwargs)
+            return
+
+        # Otherwise, first make sure the optimizer was stepped.
+        if not self.gradient_state.sync_gradients:
+            if self.gradient_state.adjust_scheduler:
+                self.scheduler._step_count += 1
+            return
+
+        for opt in self.optimizers:
+            if opt.step_was_skipped:
+                return
+        if self.split_batches:
+            # Split batches -> the training dataloader batch size is not changed so one step per training step
+            self.scheduler.step(*args, **kwargs)
+        else:
+            # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
+            # num_processes steps per training step
+            num_processes = AcceleratorState().num_processes
+            for _ in range(num_processes):
+                # Special case when using OneCycle and `drop_last` was not used
+                if hasattr(self.scheduler, "total_steps"):
+                    if self.scheduler._step_count <= self.scheduler.total_steps:
+                        self.scheduler.step(*args, **kwargs)
+                else:
+                    self.scheduler.step(*args, **kwargs)
+
+    # Passthroughs
+    def get_last_lr(self):
+        return self.scheduler.get_last_lr()
+
+    def state_dict(self):
+        return self.scheduler.state_dict()
+
+    def load_state_dict(self, state_dict):
+        self.scheduler.load_state_dict(state_dict)
+
+    def get_lr(self):
+        return self.scheduler.get_lr()
+
+    def print_lr(self, *args, **kwargs):
+        return self.scheduler.print_lr(*args, **kwargs)