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(	__A   ,       __A   ,       __A   ,       __A			):
  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(	__A   ,       __A   ,       __A   ,       __A   ,       __A=True			):
  model.train()
  UpperCAmelCase					      =  model(__A			)
  UpperCAmelCase					      =  F.mse_loss(__A   ,       target.to(output.device			)			)
  if not do_backward:
    loss /= accelerator.gradient_accumulation_steps
    loss.backward()
  else:
    accelerator.backward(__A			)
def      _lowerCAmelCase(	__A   ,       __A=False			):
  set_seed(42			)
  UpperCAmelCase					      =  RegressionModel()
  UpperCAmelCase					      =  deepcopy(__A			)
  UpperCAmelCase					      =  RegressionDataset(length=80			)
  UpperCAmelCase					      =  DataLoader(__A   ,       batch_size=16			)
  model.to(accelerator.device			)
  if sched:
    UpperCAmelCase					      =  AdamW(params=model.parameters()   ,       lr=1E-3			)
    UpperCAmelCase					      =  AdamW(params=ddp_model.parameters()   ,       lr=1E-3			)
    UpperCAmelCase					      =  LambdaLR(__A   ,       lr_lambda=lambda __A			: epoch**0.65			)
    UpperCAmelCase					      =  LambdaLR(__A   ,       lr_lambda=lambda __A			: epoch**0.65			)
  # Make a copy of `model`
  if sched:
    UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  accelerator.prepare(__A   ,       __A   ,       __A   ,       __A			)
  else:
    UpperCAmelCase      ,	UpperCAmelCase					      =  accelerator.prepare(__A   ,       __A			)
  if sched:
    return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
  return model, ddp_model, dataloader
def      _lowerCAmelCase(	__A			):
  # Test when on a single CPU or GPU that the context manager does nothing
  UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  get_training_setup(__A			)
  # Use a single batch
  UpperCAmelCase      ,	UpperCAmelCase					      =  next(iter(__A			)			).values()
  for iteration in range(3			):
    # Gather the distributed inputs and targs for the base model
    UpperCAmelCase      ,	UpperCAmelCase					      =  accelerator.gather((ddp_input, ddp_target)			)
    UpperCAmelCase      ,	UpperCAmelCase					      =  input.to(accelerator.device			), target.to(accelerator.device			)
    # Perform our initial ground truth step in non "DDP"
    step_model(__A   ,       __A   ,       __A   ,       __A			)
    # Do "gradient accumulation" (noop)
    if iteration % 2 == 0:
      # Accumulate grads locally
      with accelerator.no_sync(__A			):
        step_model(__A   ,       __A   ,       __A   ,       __A			)
    else:
      # Sync grads
      step_model(__A   ,       __A   ,       __A   ,       __A			)

    # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
    check_model_parameters(__A   ,       __A   ,       __A   ,       __A			)
    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			)
    UpperCAmelCase					      =  ddp_input[torch.randperm(len(__A			)			)]
def      _lowerCAmelCase(	__A			):
  # Test on distributed setup that context manager behaves properly
  UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  get_training_setup(__A			)
  # Use a single batch
  UpperCAmelCase      ,	UpperCAmelCase					      =  next(iter(__A			)			).values()
  for iteration in range(3			):
    # Gather the distributed inputs and targs for the base model
    UpperCAmelCase      ,	UpperCAmelCase					      =  accelerator.gather((ddp_input, ddp_target)			)
    UpperCAmelCase      ,	UpperCAmelCase					      =  input.to(accelerator.device			), target.to(accelerator.device			)
    # Perform our initial ground truth step in non "DDP"
    step_model(__A   ,       __A   ,       __A   ,       __A			)
    # Do "gradient accumulation" (noop)
    if iteration % 2 == 0:
      # Accumulate grads locally
      with accelerator.no_sync(__A			):
        step_model(__A   ,       __A   ,       __A   ,       __A			)
    else:
      # Sync grads
      step_model(__A   ,       __A   ,       __A   ,       __A			)

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

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

    if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__A			)):
      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(__A			):
      step_model(__A   ,       __A   ,       __A   ,       __A			)
      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"
    UpperCAmelCase					      =  (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__A			))
    if accelerator.num_processes > 1:
      check_model_parameters(__A   ,       __A   ,       __A   ,       __A			)
    # Shuffle ddp_input on each iteration
    torch.manual_seed(1337 + iteration			)
  GradientState._reset_state()
def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  Accelerator()

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

    # 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(__A   ,       __A			)
def      _lowerCAmelCase(	__A			):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()






import numpy

# List of input, output pairs
lowerCAmelCase__       = (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCAmelCase__       = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__       = [2, 4, 1, 5]
lowerCAmelCase__       = len(train_data)
lowerCAmelCase__       = 0.0_0_9
def      _lowerCAmelCase(	__A   ,       __A="train"			):
  return calculate_hypothesis_value(__A   ,       __A			) - output(
      __A   ,       __A			)
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  0
  for i in range(len(__A			) - 1			):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def      _lowerCAmelCase(	__A   ,       __A			):
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None
def      _lowerCAmelCase(	__A   ,       __A			):
  if data_set == "train":
    return _hypothesis_value(train_data[example_no][0]			)
  elif data_set == "test":
    return _hypothesis_value(test_data[example_no][0]			)
  return None
def      _lowerCAmelCase(	__A   ,       __A=m			):
  UpperCAmelCase					      =  0
  for i in range(__A			):
    if index == -1:
      summation_value += _error(__A			)
    else:
      summation_value += _error(__A			) * train_data[i][0][index]
  return summation_value
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  summation_of_cost_derivative(__A   ,       __A			) / m
  return cost_derivative_value
def      _lowerCAmelCase(	):
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  UpperCAmelCase					      =  0.000002
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  0
  while True:
    j += 1
    UpperCAmelCase					      =  [0, 0, 0, 0]
    for i in range(0   ,       len(__A			)			):
      UpperCAmelCase					      =  get_cost_derivative(i - 1			)
      UpperCAmelCase					      =  (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        __A   ,       __A   ,       atol=__A   ,       rtol=__A   ,       ):
      break
    UpperCAmelCase					      =  temp_parameter_vector
  print(("Number of iterations:", j)			)
def      _lowerCAmelCase(	):
  for i in range(len(__A			)			):
    print(("Actual output value:", output(__A   ,       "test"			))			)
    print(("Hypothesis output:", calculate_hypothesis_value(__A   ,       "test"			))			)


if __name__ == "__main__":
      run_gradient_descent()
      print("\nTesting gradient descent for a linear hypothesis function.\n")
      test_gradient_descent()



