






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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Union[str, 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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=True				)     ->    Tuple:
							model.train()
							_lowercase							:   List[str] 							=   model(SCREAMING_SNAKE_CASE				)
							_lowercase							:   Union[str, Any] 							=   F.mse_loss(SCREAMING_SNAKE_CASE     ,     target.to(output.device				)				)
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(SCREAMING_SNAKE_CASE				)






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






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

														# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
														check_model_parameters(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_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(1_337 + iteration				)
														_lowercase							:   List[Any] 							=   ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE				)				)]






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Tuple:
							# Test on distributed setup that context manager behaves properly
							_lowercase							,							_lowercase							,							_lowercase							:   Dict 							=   get_training_setup(SCREAMING_SNAKE_CASE				)
							# Use a single batch
							_lowercase							,							_lowercase							:   str 							=   next(iter(SCREAMING_SNAKE_CASE				)				).values()
							for iteration in range(3				):
														# Gather the distributed inputs and targs for the base model
														_lowercase							,							_lowercase							:   str 							=   accelerator.gather((ddp_input, ddp_target)				)
														_lowercase							,							_lowercase							:   List[Any] 							=   input.to(accelerator.device				), target.to(accelerator.device				)
														# Perform our initial ground truth step in non "DDP"
														step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(SCREAMING_SNAKE_CASE				):
																												step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														else:
																					# Sync grads
																					step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_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(1_337 + iteration				)
														_lowercase							:   int 							=   ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE				)				)]






def    __magic_name__    (	SCREAMING_SNAKE_CASE=False     ,     SCREAMING_SNAKE_CASE=False				)     ->    List[str]:
							_lowercase							:   List[str] 							=   Accelerator(
							    split_batches=SCREAMING_SNAKE_CASE     ,     dispatch_batches=SCREAMING_SNAKE_CASE     ,     gradient_accumulation_steps=2				)
							# Test that context manager behaves properly
							_lowercase							,							_lowercase							,							_lowercase							:   Any 							=   get_training_setup(SCREAMING_SNAKE_CASE				)
							for iteration, batch in enumerate(SCREAMING_SNAKE_CASE				):
														_lowercase							,							_lowercase							:   int 							=   batch.values()
														# Gather the distributed inputs and targs for the base model
														_lowercase							,							_lowercase							:   Tuple 							=   accelerator.gather((ddp_input, ddp_target)				)
														_lowercase							,							_lowercase							:   List[str] 							=   input.to(accelerator.device				), target.to(accelerator.device				)
														# Perform our initial ground truth step in non "DDP"
														step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														# Do "gradient accumulation" (noop)
														with accelerator.accumulate(SCREAMING_SNAKE_CASE				):
																					step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_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(SCREAMING_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(1_337 + iteration				)
														_lowercase							:   Optional[int] 							=   ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE				)				)]
							GradientState._reset_state()






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

														if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(SCREAMING_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(SCREAMING_SNAKE_CASE				):
																					step_model(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_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"""
														_lowercase							:   Optional[int] 							=   (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(SCREAMING_SNAKE_CASE				))
														if accelerator.num_processes > 1:
																					check_model_parameters(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1_337 + iteration				)
							GradientState._reset_state()






def    __magic_name__    (	)     ->    Any:
							_lowercase							:   Any 							=   Accelerator()

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






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Union[str, Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
							main()









import os
import time

import numpy as np
import onnxruntime as ort


__snake_case     :Any			       ='1'
__snake_case     :List[str]			       ='0'
__snake_case     :Union[str, Any]			       ='1'

__snake_case     :Optional[Any]			       =ort.SessionOptions()
__snake_case     :List[str]			       =ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
__snake_case     :int			       =['TensorrtExecutionProvider', 'CUDAExecutionProvider']
__snake_case     :Optional[Any]			       =ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
__snake_case     :int			       =ort.RunOptions()

__snake_case     :Tuple			       =128
__snake_case     :List[Any]			       =1
__snake_case     :Union[str, Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[int]			       =np.ones((batch, sequence), dtype=np.intaa)

print('Warm up phase...')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('Start inference...')
__snake_case     :Union[str, Any]			       =time.time()
__snake_case     :int			       =2000
__snake_case     :Optional[int]			       ={}
for iter in range(max_iters):
				__snake_case     :Optional[Any]			       =sess.run(
				    None,
				    {
				        sess.get_inputs()[0].name: input_ids,
				        sess.get_inputs()[1].name: attention_mask,
				        sess.get_inputs()[2].name: token_type_ids,
				    },
				    run_options=run_opt,
				)
print('Average Inference Time = {:.3f} ms'.format((time.time() - start_time) * 1000 / max_iters))