



"""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       _snake_case						(						UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		str			,		UpperCamelCase						:		Optional[int]						):
 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       _snake_case						(						UpperCamelCase						:		List[str]			,		UpperCamelCase						:		str			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		int			,		UpperCamelCase						:		Optional[Any]=True						):
 model.train()
 UpperCAmelCase	:					str	=	model(UpperCamelCase						)
 UpperCAmelCase	:					Optional[int]	=	F.mse_loss(UpperCamelCase			,		target.to(output.device						)						)
 if not do_backward:
  loss /= accelerator.gradient_accumulation_steps
  loss.backward()
 else:
  accelerator.backward(UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		List[Any]=False						):
 set_seed(42						)
 UpperCAmelCase	:					Tuple	=	RegressionModel()
 UpperCAmelCase	:					List[Any]	=	deepcopy(UpperCamelCase						)
 UpperCAmelCase	:					Optional[Any]	=	RegressionDataset(length=80						)
 UpperCAmelCase	:					Union[str, Any]	=	DataLoader(UpperCamelCase			,		batch_size=16						)
 model.to(accelerator.device						)
 if sched:
  UpperCAmelCase	:					Union[str, Any]	=	AdamW(params=model.parameters()			,		lr=1e-3						)
  UpperCAmelCase	:					List[str]	=	AdamW(params=ddp_model.parameters()			,		lr=1e-3						)
  UpperCAmelCase	:					Tuple	=	LambdaLR(UpperCamelCase			,		lr_lambda=lambda UpperCamelCase						: epoch**0.65						)
  UpperCAmelCase	:					Tuple	=	LambdaLR(UpperCamelCase			,		lr_lambda=lambda UpperCamelCase						: epoch**0.65						)
 # Make a copy of `model`
 if sched:
  UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					List[Any]	=	accelerator.prepare(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
 else:
  UpperCAmelCase	,		UpperCAmelCase	:					int	=	accelerator.prepare(UpperCamelCase			,		UpperCamelCase						)
 if sched:
  return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
 return model, ddp_model, dataloader

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

  # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
  check_model_parameters(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
  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	:					List[str]	=	ddp_input[torch.randperm(len(UpperCamelCase						)						)]

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

  # 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	:					Optional[int]	=	ddp_input[torch.randperm(len(UpperCamelCase						)						)]

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

  # 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(UpperCamelCase						) - 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	:					str	=	ddp_input[torch.randperm(len(UpperCamelCase						)						)]
 GradientState._reset_state()

def       _snake_case						(						UpperCamelCase						:		List[Any]=False			,		UpperCamelCase						:		List[Any]=False						):
 UpperCAmelCase	:					Optional[Any]	=	Accelerator(
     split_batches=UpperCamelCase			,		dispatch_batches=UpperCamelCase			,		gradient_accumulation_steps=2						)
 # Test that context manager behaves properly
 UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	get_training_setup(UpperCamelCase			,		UpperCamelCase						)
 for iteration, batch in enumerate(UpperCamelCase						):
  UpperCAmelCase	,		UpperCAmelCase	:					Any	=	batch.values()
  # Gather the distributed inputs and targs for the base model
  UpperCAmelCase	,		UpperCAmelCase	:					Any	=	accelerator.gather((ddp_input, ddp_target)						)
  UpperCAmelCase	,		UpperCAmelCase	:					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(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
  opt.step()

  if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase						)):
   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(UpperCamelCase						):
   step_model(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
   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	:					List[str]	=	(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase						))
  if accelerator.num_processes > 1:
   check_model_parameters(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
  # Shuffle ddp_input on each iteration
  torch.manual_seed(1337 + iteration						)
 GradientState._reset_state()

def       _snake_case						(						):
 UpperCAmelCase	:					str	=	Accelerator()

 UpperCAmelCase	:					Any	=	RegressionDataset(length=80						)
 UpperCAmelCase	:					Tuple	=	DataLoader(UpperCamelCase			,		batch_size=16						)
 UpperCAmelCase	:					List[Any]	=	RegressionDataset(length=96						)
 UpperCAmelCase	:					str	=	DataLoader(UpperCamelCase			,		batch_size=16						)
 UpperCAmelCase	,		UpperCAmelCase	:					Union[str, Any]	=	accelerator.prepare(UpperCamelCase			,		UpperCamelCase						)
 assert accelerator.gradient_state.active_dataloader is None
 for iteration, _ in enumerate(UpperCamelCase						):
  assert id(accelerator.gradient_state.active_dataloader						) == id(UpperCamelCase						)
  if iteration < len(UpperCamelCase						) - 1:
   assert not accelerator.gradient_state.end_of_dataloader
   if iteration == 1:
    for batch_num, _ in enumerate(UpperCamelCase						):
     assert id(accelerator.gradient_state.active_dataloader						) == id(UpperCamelCase						)
     if batch_num < len(UpperCamelCase						) - 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       _snake_case						(						):
 UpperCAmelCase	:					Dict	=	Accelerator()
 UpperCAmelCase	:					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(UpperCamelCase						)
 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(UpperCamelCase						)
 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(UpperCamelCase			,		UpperCamelCase						)

    # 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(UpperCamelCase			,		UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		List[str]						):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()






"""simple docstring"""






import functools
from typing import Any







def      _snake_case			(				lowercase__							,    lowercase__		):

				# Validation
				if not isinstance(lowercase__							,    lowercase__		) or len(lowercase__		) == 0:
								raise ValueError('the string should be not empty string'		)

				if not isinstance(lowercase__							,    lowercase__		) or not all(
				    isinstance(lowercase__							,    lowercase__		) and len(lowercase__		) > 0 for item in words		):
								raise ValueError('the words should be a list of non-empty strings'		)

				# Build trie
				_lowerCamelCase     :			dict[str, Any]    =    {}
				_lowerCamelCase     :			List[Any]    =    'WORD_KEEPER'

				for word in words:
								_lowerCamelCase     :			Dict    =    trie
								for c in word:
												if c not in trie_node:
																_lowerCamelCase     :			Any    =    {}

												_lowerCamelCase     :			str    =    trie_node[c]

								_lowerCamelCase     :			Optional[Any]    =    True

				_lowerCamelCase     :			Dict    =    len(lowercase__		)

				# Dynamic programming method
				@functools.cache
				def is_breakable(lowercase__		) -> bool:
								if index == len_string:
												return True

								_lowerCamelCase     :			List[Any]    =    trie
								for i in range(lowercase__							,    lowercase__		):
												_lowerCamelCase     :			Any    =    trie_node.get(string[i]							,    lowercase__		)

												if trie_node is None:
																return False

												if trie_node.get(lowercase__							,    lowercase__		) and is_breakable(i + 1		):
																return True

								return False

				return is_breakable(0		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()