



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        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     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        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase=True   )     ->     int:
					model.train()
					SCREAMING_SNAKE_CASE							:     Dict						=				model(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Dict						=				F.mse_loss(__lowerCAmelCase      ,	target.to(output.device   )   )
					if not do_backward:
										loss /= accelerator.gradient_accumulation_steps
										loss.backward()
					else:
										accelerator.backward(__lowerCAmelCase   )

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

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

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

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

										# 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   )
										SCREAMING_SNAKE_CASE							:     Tuple						=				ddp_input[torch.randperm(len(__lowerCAmelCase   )   )]

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

										# 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(__lowerCAmelCase   ) - 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   )
										SCREAMING_SNAKE_CASE							:     List[Any]						=				ddp_input[torch.randperm(len(__lowerCAmelCase   )   )]
					GradientState._reset_state()

def        __a			(   __lowerCAmelCase=False      ,	__lowerCAmelCase=False   )     ->     Optional[int]:
					SCREAMING_SNAKE_CASE							:     Tuple						=				Accelerator(
					    split_batches=__lowerCAmelCase      ,	dispatch_batches=__lowerCAmelCase      ,	gradient_accumulation_steps=2   )
					# Test that context manager behaves properly
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     List[str]						=				get_training_setup(__lowerCAmelCase      ,	__lowerCAmelCase   )
					for iteration, batch in enumerate(__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[int]						=				batch.values()
										# Gather the distributed inputs and targs for the base model
										SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     List[str]						=				accelerator.gather((ddp_input, ddp_target)   )
										SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     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(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
										opt.step()

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

def        __a			(   )     ->     Union[str, Any]:
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				Accelerator()

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

    # 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(__lowerCAmelCase      ,	__lowerCAmelCase   )

def        __a			(   __lowerCAmelCase   )     ->     str:
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
			main()




_lowerCamelCase    :		Optional[Any]		      =    256
# Modulus to hash a string
_lowerCamelCase    :		Optional[Any]		      =    1_000_003

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     bool:
					SCREAMING_SNAKE_CASE							:     Tuple						=				len(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Tuple						=				len(__lowerCAmelCase   )
					if p_len > t_len:
										return False

					SCREAMING_SNAKE_CASE							:     Dict						=				0
					SCREAMING_SNAKE_CASE							:     Tuple						=				0
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				1

					# Calculating the hash of pattern and substring of text
					for i in range(__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     Tuple						=				(ord(pattern[i]   ) + p_hash * alphabet_size) % modulus
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				(ord(text[i]   ) + text_hash * alphabet_size) % modulus
										if i == p_len - 1:
															continue
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				(modulus_power * alphabet_size) % modulus

					for i in range(0      ,	t_len - p_len + 1   ):
										if text_hash == p_hash and text[i : i + p_len] == pattern:
															return True
										if i == t_len - p_len:
															continue
										# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
										SCREAMING_SNAKE_CASE							:     int						=				(
										    (text_hash - ord(text[i]   ) * modulus_power) * alphabet_size
										    + ord(text[i + p_len]   )
										) % modulus
					return False

def        __a			(   )     ->     None:
					SCREAMING_SNAKE_CASE							:     Any						=				'abc1abc12'
					SCREAMING_SNAKE_CASE							:     str						=				'alskfjaldsabc1abc1abc12k23adsfabcabc'
					SCREAMING_SNAKE_CASE							:     List[str]						=				'alskfjaldsk23adsfabcabc'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   ) and not rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )

					# Test 2)
					SCREAMING_SNAKE_CASE							:     List[str]						=				'ABABX'
					SCREAMING_SNAKE_CASE							:     int						=				'ABABZABABYABABX'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )

					# Test 3)
					SCREAMING_SNAKE_CASE							:     int						=				'AAAB'
					SCREAMING_SNAKE_CASE							:     Tuple						=				'ABAAAAAB'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )

					# Test 4)
					SCREAMING_SNAKE_CASE							:     Tuple						=				'abcdabcy'
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				'abcxabcdabxabcdabcdabcy'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )

					# Test 5)
					SCREAMING_SNAKE_CASE							:     List[Any]						=				'Lü'
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				'Lüsai'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'Lue'
					assert not rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )
					print('Success.'   )


if __name__ == "__main__":
			test_rabin_karp()