



"""simple docstring"""
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__	)			->      Optional[Any]:
  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	)			->      Tuple:
  model.train()
  a_     :      Dict 						=     model(__A	)
  a_     :      List[Any] 						=     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_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=False	)			->      Optional[Any]:
  set_seed(42	)
  a_     :      List[Any] 						=     RegressionModel()
  a_     :      Optional[int] 						=     deepcopy(__A	)
  a_     :      Optional[Any] 						=     RegressionDataset(length=80	)
  a_     :      List[Any] 						=     DataLoader(__A, batch_size=16	)
  model.to(accelerator.device	)
  if sched:
    a_     :      int 						=     AdamW(params=model.parameters(), lr=1e-3	)
    a_     :      int 						=     AdamW(params=ddp_model.parameters(), lr=1e-3	)
    a_     :      List[str] 						=     LambdaLR(__A, lr_lambda=lambda SCREAMING_SNAKE_CASE__	: epoch**0.65	)
    a_     :      int 						=     LambdaLR(__A, lr_lambda=lambda SCREAMING_SNAKE_CASE__	: epoch**0.65	)
  # Make a copy of `model`
  if sched:
    a_						,				a_						,				a_						,				a_     :      Dict 						=     accelerator.prepare(__A, __A, __A, __A	)
  else:
    a_						,				a_     :      Tuple 						=     accelerator.prepare(__A, __A	)
  if sched:
    return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
  return model, ddp_model, dataloader



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      List[Any]:
  # Test when on a single CPU or GPU that the context manager does nothing
  a_						,				a_						,				a_     :      int 						=     get_training_setup(__A	)
  # Use a single batch
  a_						,				a_     :      List[str] 						=     next(iter(__A	)	).values()
  for iteration in range(3	):
    # Gather the distributed inputs and targs for the base model
    a_						,				a_     :      Dict 						=     accelerator.gather((ddp_input, ddp_target)	)
    a_						,				a_     :      int 						=     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(1_337 + iteration	)
    a_     :      str 						=     ddp_input[torch.randperm(len(__A	)	)]



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Dict:
  # Test on distributed setup that context manager behaves properly
  a_						,				a_						,				a_     :      Dict 						=     get_training_setup(__A	)
  # Use a single batch
  a_						,				a_     :      str 						=     next(iter(__A	)	).values()
  for iteration in range(3	):
    # Gather the distributed inputs and targs for the base model
    a_						,				a_     :      List[str] 						=     accelerator.gather((ddp_input, ddp_target)	)
    a_						,				a_     :      str 						=     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(1_337 + iteration	)
    a_     :      Tuple 						=     ddp_input[torch.randperm(len(__A	)	)]



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__=False, SCREAMING_SNAKE_CASE__=False	)			->      str:
  a_     :      Optional[Any] 						=     Accelerator(
      split_batches=__A, dispatch_batches=__A, gradient_accumulation_steps=2	)
  # Test that context manager behaves properly
  a_						,				a_						,				a_     :      Any 						=     get_training_setup(__A	)
  for iteration, batch in enumerate(__A	):
    a_						,				a_     :      List[Any] 						=     batch.values()
    # Gather the distributed inputs and targs for the base model
    a_						,				a_     :      Optional[Any] 						=     accelerator.gather((ddp_input, ddp_target)	)
    a_						,				a_     :      Dict 						=     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(1_337 + iteration	)
    a_     :      Optional[Any] 						=     ddp_input[torch.randperm(len(__A	)	)]
  GradientState._reset_state()



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__=False, SCREAMING_SNAKE_CASE__=False	)			->      str:
  a_     :      Union[str, Any] 						=     Accelerator(
      split_batches=__A, dispatch_batches=__A, gradient_accumulation_steps=2	)
  # Test that context manager behaves properly
  a_						,				a_						,				a_						,				a_						,				a_						,				a_						,				a_     :      List[Any] 						=     get_training_setup(__A, __A	)
  for iteration, batch in enumerate(__A	):
    a_						,				a_     :      List[str] 						=     batch.values()
    # Gather the distributed inputs and targs for the base model
    a_						,				a_     :      Union[str, Any] 						=     accelerator.gather((ddp_input, ddp_target)	)
    a_						,				a_     :      Optional[int] 						=     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"""
    a_     :      str 						=     (((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(1_337 + iteration	)
  GradientState._reset_state()



def        lowerCAmelCase_						(							)			->      Tuple:
  a_     :      int 						=     Accelerator()

  a_     :      Dict 						=     RegressionDataset(length=80	)
  a_     :      List[Any] 						=     DataLoader(__A, batch_size=16	)
  a_     :      Union[str, Any] 						=     RegressionDataset(length=96	)
  a_     :      Optional[int] 						=     DataLoader(__A, batch_size=16	)
  a_						,				a_     :      List[Any] 						=     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_						(							)			->      List[str]:
  a_     :      Tuple 						=     Accelerator()
  a_     :      Optional[Any] 						=     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_						(							SCREAMING_SNAKE_CASE__	)			->      int:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
     main()




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


SCREAMING_SNAKE_CASE_				= False




class 			snake_case_						(						unittest.TestCase				):
							pass




@slow
@require_torch_gpu
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"      )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Optional[Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
									a_     :      Optional[Any] 						=     torch.manual_seed(0      )
									a_     :      Dict 						=     pipe(
									    image=a_			,     generator=a_			,     guidance_scale=7.5			,     num_inference_steps=5_0			,     output_type="numpy"			,     ).images

									a_     :      List[Any] 						=     image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									a_     :      Any 						=     np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2