



from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
   for param, grad_param in zip(model_a.parameters()						,  model_b.parameters()  ):
      if not param.requires_grad:
         continue
      if not did_step:
         # Grads should not be in sync
         assert (
             torch.allclose(param.grad						,  grad_param.grad  ) is False
         ), f'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
      else:
         # Grads should be in sync
         assert (
             torch.allclose(param.grad						,  grad_param.grad  ) is True
         ), f'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=True  ):
   model.train()
   lowercase__					  =				model(SCREAMING_SNAKE_CASE_  )
   lowercase__					  =				F.mse_loss(SCREAMING_SNAKE_CASE_						,  target.to(output.device  )  )
   if not do_backward:
      loss /= accelerator.gradient_accumulation_steps
      loss.backward()
   else:
      accelerator.backward(SCREAMING_SNAKE_CASE_  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=False  ):
   set_seed(42  )
   lowercase__					  =				RegressionModel()
   lowercase__					  =				deepcopy(SCREAMING_SNAKE_CASE_  )
   lowercase__					  =				RegressionDataset(length=80  )
   lowercase__					  =				DataLoader(SCREAMING_SNAKE_CASE_						,  batch_size=16  )
   model.to(accelerator.device  )
   if sched:
      lowercase__					  =				AdamW(params=model.parameters()						,  lr=1e-3  )
      lowercase__					  =				AdamW(params=ddp_model.parameters()						,  lr=1e-3  )
      lowercase__					  =				LambdaLR(SCREAMING_SNAKE_CASE_						,  lr_lambda=lambda SCREAMING_SNAKE_CASE_  : epoch**0.65  )
      lowercase__					  =				LambdaLR(SCREAMING_SNAKE_CASE_						,  lr_lambda=lambda SCREAMING_SNAKE_CASE_  : epoch**0.65  )
   # Make a copy of `model`
   if sched:
      lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__					  =				accelerator.prepare(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
   else:
      lowercase__  ,       lowercase__					  =				accelerator.prepare(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
   if sched:
      return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
   return model, ddp_model, dataloader



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
   # Test when on a single CPU or GPU that the context manager does nothing
   lowercase__  ,       lowercase__  ,       lowercase__					  =				get_training_setup(SCREAMING_SNAKE_CASE_  )
   # Use a single batch
   lowercase__  ,       lowercase__					  =				next(iter(SCREAMING_SNAKE_CASE_  )  ).values()
   for iteration in range(3  ):
      # Gather the distributed inputs and targs for the base model
      lowercase__  ,       lowercase__					  =				accelerator.gather((ddp_input, ddp_target)  )
      lowercase__  ,       lowercase__					  =				input.to(accelerator.device  ), target.to(accelerator.device  )
      # Perform our initial ground truth step in non "DDP"
      step_model(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
      # Do "gradient accumulation" (noop)
      if iteration % 2 == 0:
         # Accumulate grads locally
         with accelerator.no_sync(SCREAMING_SNAKE_CASE_  ):
            step_model(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
      else:
         # Sync grads
         step_model(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )

      # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
      check_model_parameters(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
      for param, ddp_param in zip(model.parameters()						,  ddp_model.parameters()  ):
         if not param.requires_grad:
            continue
         assert torch.allclose(
             param.grad						,  ddp_param.grad  ), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

      # Shuffle ddp_input on each iteration
      torch.manual_seed(1337 + iteration  )
      lowercase__					  =				ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE_  )  )]



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
   # Test on distributed setup that context manager behaves properly
   lowercase__  ,       lowercase__  ,       lowercase__					  =				get_training_setup(SCREAMING_SNAKE_CASE_  )
   # Use a single batch
   lowercase__  ,       lowercase__					  =				next(iter(SCREAMING_SNAKE_CASE_  )  ).values()
   for iteration in range(3  ):
      # Gather the distributed inputs and targs for the base model
      lowercase__  ,       lowercase__					  =				accelerator.gather((ddp_input, ddp_target)  )
      lowercase__  ,       lowercase__					  =				input.to(accelerator.device  ), target.to(accelerator.device  )
      # Perform our initial ground truth step in non "DDP"
      step_model(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
      # Do "gradient accumulation" (noop)
      if iteration % 2 == 0:
         # Accumulate grads locally
         with accelerator.no_sync(SCREAMING_SNAKE_CASE_  ):
            step_model(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
      else:
         # Sync grads
         step_model(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )

      # DDP model and model should only be in sync when not (iteration % 2 == 0)
      for param, ddp_param in zip(model.parameters()						,  ddp_model.parameters()  ):
         if not param.requires_grad:
            continue
         if iteration % 2 == 0:
            # Grads should not be in sync
            assert (
                torch.allclose(param.grad						,  ddp_param.grad  ) is False
            ), f'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
         else:
            # Grads should be in sync
            assert (
                torch.allclose(param.grad						,  ddp_param.grad  ) is True
            ), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
      torch.manual_seed(1337 + iteration  )
      lowercase__					  =				ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE_  )  )]



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_=False						,  SCREAMING_SNAKE_CASE_=False  ):
   lowercase__					  =				Accelerator(
       split_batches=SCREAMING_SNAKE_CASE_						,  dispatch_batches=SCREAMING_SNAKE_CASE_						,  gradient_accumulation_steps=2  )
   # Test that context manager behaves properly
   lowercase__  ,       lowercase__  ,       lowercase__					  =				get_training_setup(SCREAMING_SNAKE_CASE_  )
   for iteration, batch in enumerate(SCREAMING_SNAKE_CASE_  ):
      lowercase__  ,       lowercase__					  =				batch.values()
      # Gather the distributed inputs and targs for the base model
      lowercase__  ,       lowercase__					  =				accelerator.gather((ddp_input, ddp_target)  )
      lowercase__  ,       lowercase__					  =				input.to(accelerator.device  ), target.to(accelerator.device  )
      # Perform our initial ground truth step in non "DDP"
      step_model(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
      # Do "gradient accumulation" (noop)
      with accelerator.accumulate(SCREAMING_SNAKE_CASE_  ):
         step_model(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )

      # DDP model and model should only be in sync when not (iteration % 2 == 0)
      for param, ddp_param in zip(model.parameters()						,  ddp_model.parameters()  ):
         if not param.requires_grad:
            continue
         if ((iteration + 1) % 2 == 0) or (iteration == len(SCREAMING_SNAKE_CASE_  ) - 1):
            # Grads should be in sync
            assert (
                torch.allclose(param.grad						,  ddp_param.grad  ) is True
            ), f'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
         else:
            # Grads should not be in sync
            assert (
                torch.allclose(param.grad						,  ddp_param.grad  ) is False
            ), f'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
      torch.manual_seed(1337 + iteration  )
      lowercase__					  =				ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE_  )  )]
   GradientState._reset_state()



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_=False						,  SCREAMING_SNAKE_CASE_=False  ):
   lowercase__					  =				Accelerator(
       split_batches=SCREAMING_SNAKE_CASE_						,  dispatch_batches=SCREAMING_SNAKE_CASE_						,  gradient_accumulation_steps=2  )
   # Test that context manager behaves properly
   lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__					  =				get_training_setup(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
   for iteration, batch in enumerate(SCREAMING_SNAKE_CASE_  ):
      lowercase__  ,       lowercase__					  =				batch.values()
      # Gather the distributed inputs and targs for the base model
      lowercase__  ,       lowercase__					  =				accelerator.gather((ddp_input, ddp_target)  )
      lowercase__  ,       lowercase__					  =				input.to(accelerator.device  ), target.to(accelerator.device  )
      # Perform our initial ground truth step in non "DDP"
      model.train()
      ddp_model.train()
      step_model(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
      opt.step()

      if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(SCREAMING_SNAKE_CASE_  )):
         if split_batches:
            sched.step()
         else:
            for _ in range(accelerator.num_processes  ):
               sched.step()
      opt.zero_grad()
      # Perform gradient accumulation under wrapper
      with accelerator.accumulate(SCREAMING_SNAKE_CASE_  ):
         step_model(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
         ddp_opt.step()
         ddp_sched.step()
         ddp_opt.zero_grad()

      # Learning rates should be the same
      assert (
          opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
      ), f'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'''
      lowercase__					  =				(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(SCREAMING_SNAKE_CASE_  ))
      if accelerator.num_processes > 1:
         check_model_parameters(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
      # Shuffle ddp_input on each iteration
      torch.manual_seed(1337 + iteration  )
   GradientState._reset_state()



def __lowerCAmelCase (     ):
   lowercase__					  =				Accelerator()

   lowercase__					  =				RegressionDataset(length=80  )
   lowercase__					  =				DataLoader(SCREAMING_SNAKE_CASE_						,  batch_size=16  )
   lowercase__					  =				RegressionDataset(length=96  )
   lowercase__					  =				DataLoader(SCREAMING_SNAKE_CASE_						,  batch_size=16  )
   lowercase__  ,       lowercase__					  =				accelerator.prepare(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
   assert accelerator.gradient_state.active_dataloader is None
   for iteration, _ in enumerate(SCREAMING_SNAKE_CASE_  ):
      assert id(accelerator.gradient_state.active_dataloader  ) == id(SCREAMING_SNAKE_CASE_  )
      if iteration < len(SCREAMING_SNAKE_CASE_  ) - 1:
         assert not accelerator.gradient_state.end_of_dataloader
         if iteration == 1:
            for batch_num, _ in enumerate(SCREAMING_SNAKE_CASE_  ):
               assert id(accelerator.gradient_state.active_dataloader  ) == id(SCREAMING_SNAKE_CASE_  )
               if batch_num < len(SCREAMING_SNAKE_CASE_  ) - 1:
                  assert not accelerator.gradient_state.end_of_dataloader
               else:
                  assert accelerator.gradient_state.end_of_dataloader
      else:
         assert accelerator.gradient_state.end_of_dataloader
   assert accelerator.gradient_state.active_dataloader is None



def __lowerCAmelCase (     ):
   lowercase__					  =				Accelerator()
   lowercase__					  =				accelerator.state
   if state.local_process_index == 0:
      print("**Test `accumulate` gradient accumulation with dataloader break**"  )
   test_dataloader_break()
   if state.distributed_type == DistributedType.NO:
      if state.local_process_index == 0:
         print("**Test NOOP `no_sync` context manager**"  )
      test_noop_sync(SCREAMING_SNAKE_CASE_  )
   if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
      if state.local_process_index == 0:
         print("**Test Distributed `no_sync` context manager**"  )
      test_distributed_sync(SCREAMING_SNAKE_CASE_  )
   if state.distributed_type == DistributedType.MULTI_GPU:
      for split_batch in [True, False]:
         for dispatch_batches in [True, False]:
            if state.local_process_index == 0:
               print(
                   "**Test `accumulate` gradient accumulation, "						,  f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''						,  )
            test_gradient_accumulation(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )

    # Currently will break on torch 2.0 +, need to investigate why
   if is_torch_version("<"						,  "2.0"  ) or state.distributed_type == DistributedType.NO:
      if state.local_process_index == 0:
         print(
             "**Test `accumulate` gradient accumulation with optimizer and scheduler, "						,  "`split_batches=False`, `dispatch_batches=False`**"						,  )
      test_gradient_accumulation_with_opt_and_scheduler()
      if state.distributed_type == DistributedType.MULTI_GPU:
         for split_batch in [True, False]:
            for dispatch_batches in [True, False]:
               if not split_batch and not dispatch_batches:
                  continue
               if state.local_process_index == 0:
                  print(
                      "**Test `accumulate` gradient accumulation with optimizer and scheduler, "						,  f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''						,  )
               test_gradient_accumulation_with_opt_and_scheduler(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
       main()









# Copyright 2021 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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def __lowerCAmelCase (     ):
			lowercase__					  =				ArgumentParser("Accelerate CLI tool"						,  usage="accelerate <command> [<args>]"						,  allow_abbrev=SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				parser.add_subparsers(help="accelerate command helpers"  )

			# Register commands
			get_config_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			env_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			launch_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			tpu_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			test_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )

			# Let's go
			lowercase__					  =				parser.parse_args()

			if not hasattr(SCREAMING_SNAKE_CASE_						,  "func"  ):
						parser.print_help()
						exit(1  )

			# Run
			args.func(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
							main()




