





'''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     (    __snake_case    :					Dict					,				__snake_case    :					List[Any]					,				__snake_case    :					Optional[Any]					,				__snake_case    :					Optional[int]     )	->	List[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     (    __snake_case    :					str					,				__snake_case    :					str					,				__snake_case    :					Optional[Any]					,				__snake_case    :					Dict					,				__snake_case    :					str=True     )	->	Dict:
     model.train()
     __A		:					List[Any]						=			model(__snake_case     )
     __A		:					int						=			F.mse_loss(__snake_case					,				target.to(output.device     )     )
     if not do_backward:
          loss /= accelerator.gradient_accumulation_steps
          loss.backward()
     else:
          accelerator.backward(__snake_case     )






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






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

          # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
          check_model_parameters(__snake_case					,				__snake_case					,				__snake_case					,				__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(13_37 + iteration     )
          __A		:					Union[str, Any]						=			ddp_input[torch.randperm(len(__snake_case     )     )]






def 			_lowerCAmelCase     (    __snake_case    :					Any     )	->	Dict:
     # Test on distributed setup that context manager behaves properly
     __A					,__A					,__A		:					Tuple						=			get_training_setup(__snake_case     )
     # Use a single batch
     __A					,__A		:					Any						=			next(iter(__snake_case     )     ).values()
     for iteration in range(3     ):
          # Gather the distributed inputs and targs for the base model
          __A					,__A		:					Any						=			accelerator.gather((ddp_input, ddp_target)     )
          __A					,__A		:					Tuple						=			input.to(accelerator.device     ), target.to(accelerator.device     )
          # Perform our initial ground truth step in non "DDP"
          step_model(__snake_case					,				__snake_case					,				__snake_case					,				__snake_case     )
          # Do "gradient accumulation" (noop)
          if iteration % 2 == 0:
               # Accumulate grads locally
               with accelerator.no_sync(__snake_case     ):
                    step_model(__snake_case					,				__snake_case					,				__snake_case					,				__snake_case     )
          else:
               # Sync grads
               step_model(__snake_case					,				__snake_case					,				__snake_case					,				__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(13_37 + iteration     )
          __A		:					Any						=			ddp_input[torch.randperm(len(__snake_case     )     )]






def 			_lowerCAmelCase     (    __snake_case    :					str=False					,				__snake_case    :					Any=False     )	->	Dict:
     __A		:					Optional[Any]						=			Accelerator(
         split_batches=__snake_case					,				dispatch_batches=__snake_case					,				gradient_accumulation_steps=2     )
     # Test that context manager behaves properly
     __A					,__A					,__A		:					Tuple						=			get_training_setup(__snake_case     )
     for iteration, batch in enumerate(__snake_case     ):
          __A					,__A		:					List[Any]						=			batch.values()
          # Gather the distributed inputs and targs for the base model
          __A					,__A		:					Dict						=			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(__snake_case					,				__snake_case					,				__snake_case					,				__snake_case					,				__snake_case     )
          # Do "gradient accumulation" (noop)
          with accelerator.accumulate(__snake_case     ):
               step_model(__snake_case					,				__snake_case					,				__snake_case					,				__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(__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(13_37 + iteration     )
          __A		:					List[Any]						=			ddp_input[torch.randperm(len(__snake_case     )     )]
     GradientState._reset_state()






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

          if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__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(__snake_case     ):
               step_model(__snake_case					,				__snake_case					,				__snake_case					,				__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'
          __A		:					Union[str, Any]						=			(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__snake_case     ))
          if accelerator.num_processes > 1:
               check_model_parameters(__snake_case					,				__snake_case					,				__snake_case					,				__snake_case     )
          # Shuffle ddp_input on each iteration
          torch.manual_seed(13_37 + iteration     )
     GradientState._reset_state()






def 			_lowerCAmelCase     (    )	->	Dict:
     __A		:					Tuple						=			Accelerator()

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






def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]     )	->	Optional[int]:
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
  main()






'''simple docstring'''




from __future__ import annotations

import math


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			size
          # approximate the overall size of segment tree with given value
          __A		:					Optional[Any]						=			[0 for i in range(0		,      4 * size)]
          # create array to store lazy update
          __A		:					Optional[Any]						=			[0 for i in range(0		,      4 * size)]
          __A		:					str						=			[0 for i in range(0		,      4 * size)]  # flag for lazy update


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return idx * 2


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return idx * 2 + 1


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if left_element == right_element:
               __A		:					List[Any]						=			a[left_element - 1]
          else:
               __A		:					List[str]						=			(left_element + right_element) // 2
               self.build(self.left(_UpperCAmelCase)		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
               self.build(self.right(_UpperCAmelCase)		,      mid + 1		,      _UpperCAmelCase		,      _UpperCAmelCase)
               __A		:					Any						=			max(
                   self.segment_tree[self.left(_UpperCAmelCase)]		,      self.segment_tree[self.right(_UpperCAmelCase)])


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if self.flag[idx] is True:
               __A		:					Optional[Any]						=			self.lazy[idx]
               __A		:					Optional[Any]						=			False
               if left_element != right_element:
                    __A		:					List[Any]						=			self.lazy[idx]
                    __A		:					Dict						=			self.lazy[idx]
                    __A		:					Tuple						=			True
                    __A		:					Union[str, Any]						=			True

          if right_element < a or left_element > b:
               return True
          if left_element >= a and right_element <= b:
               __A		:					Optional[int]						=			val
               if left_element != right_element:
                    __A		:					Tuple						=			val
                    __A		:					Any						=			val
                    __A		:					Tuple						=			True
                    __A		:					Union[str, Any]						=			True
               return True
          __A		:					str						=			(left_element + right_element) // 2
          self.update(self.left(_UpperCAmelCase)		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          self.update(self.right(_UpperCAmelCase)		,      mid + 1		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          __A		:					int						=			max(
              self.segment_tree[self.left(_UpperCAmelCase)]		,      self.segment_tree[self.right(_UpperCAmelCase)])
          return True


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if self.flag[idx] is True:
               __A		:					Union[str, Any]						=			self.lazy[idx]
               __A		:					List[str]						=			False
               if left_element != right_element:
                    __A		:					Union[str, Any]						=			self.lazy[idx]
                    __A		:					Optional[int]						=			self.lazy[idx]
                    __A		:					str						=			True
                    __A		:					Union[str, Any]						=			True
          if right_element < a or left_element > b:
               return -math.inf
          if left_element >= a and right_element <= b:
               return self.segment_tree[idx]
          __A		:					Any						=			(left_element + right_element) // 2
          __A		:					int						=			self.query(self.left(_UpperCAmelCase)		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          __A		:					Union[str, Any]						=			self.query(self.right(_UpperCAmelCase)		,      mid + 1		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          return max(_UpperCAmelCase		,      _UpperCAmelCase)


     def __str__(		self):

          '''simple docstring'''






          return str([self.query(1		,      1		,      self.size		,      _UpperCAmelCase		,      _UpperCAmelCase) for i in range(1		,      self.size + 1)])


if __name__ == "__main__":
  lowercase__       : Union[str, Any]         =       [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
  lowercase__       : str         =       15
  lowercase__       : List[Any]         =       SegmentTree(size)
  segt.build(1, 1, size, A)
  print(segt.query(1, 1, size, 4, 6))
  print(segt.query(1, 1, size, 7, 11))
  print(segt.query(1, 1, size, 7, 12))
  segt.update(1, 1, size, 1, 3, 1_11)
  print(segt.query(1, 1, size, 1, 15))
  segt.update(1, 1, size, 7, 8, 2_35)
  print(segt)