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			):
    '''simple docstring'''







    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			):
    '''simple docstring'''







    model.train()
    __UpperCamelCase  :List[str]    						=	model(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Dict    						=	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 lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False			):
    '''simple docstring'''







    set_seed(42			)
    __UpperCamelCase  :Optional[int]    						=	RegressionModel()
    __UpperCamelCase  :Union[str, Any]    						=	deepcopy(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Tuple    						=	RegressionDataset(length=80			)
    __UpperCamelCase  :Optional[Any]    						=	DataLoader(SCREAMING_SNAKE_CASE						,    batch_size=16			)
    model.to(accelerator.device			)
    if sched:
        __UpperCamelCase  :int    						=	AdamW(params=model.parameters()						,    lr=1e-3			)
        __UpperCamelCase  :Any    						=	AdamW(params=ddp_model.parameters()						,    lr=1e-3			)
        __UpperCamelCase  :List[Any]    						=	LambdaLR(SCREAMING_SNAKE_CASE						,    lr_lambda=lambda SCREAMING_SNAKE_CASE			: epoch**0.65			)
        __UpperCamelCase  :Dict    						=	LambdaLR(SCREAMING_SNAKE_CASE						,    lr_lambda=lambda SCREAMING_SNAKE_CASE			: epoch**0.65			)
    # Make a copy of `model`
    if sched:
        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :int    						=	accelerator.prepare(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    else:
        __UpperCamelCase						, __UpperCamelCase  :List[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 lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	get_training_setup(SCREAMING_SNAKE_CASE			)
    # Use a single batch
    __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	next(iter(SCREAMING_SNAKE_CASE			)			).values()
    for iteration in range(3			):
        # Gather the distributed inputs and targs for the base model
        __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	accelerator.gather((ddp_input, ddp_target)			)
        __UpperCamelCase						, __UpperCamelCase  :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			)
        # 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			)
        __UpperCamelCase  :str    						=	ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE			)			)]



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :List[str]    						=	get_training_setup(SCREAMING_SNAKE_CASE			)
    # Use a single batch
    __UpperCamelCase						, __UpperCamelCase  :Tuple    						=	next(iter(SCREAMING_SNAKE_CASE			)			).values()
    for iteration in range(3			):
        # Gather the distributed inputs and targs for the base model
        __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	accelerator.gather((ddp_input, ddp_target)			)
        __UpperCamelCase						, __UpperCamelCase  :Union[str, 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			)
        __UpperCamelCase  :str    						=	ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE			)			)]



def lowerCamelCase						(     SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=False			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	Accelerator(
        split_batches=SCREAMING_SNAKE_CASE						,    dispatch_batches=SCREAMING_SNAKE_CASE						,    gradient_accumulation_steps=2			)
    # Test that context manager behaves properly
    __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Dict    						=	get_training_setup(SCREAMING_SNAKE_CASE			)
    for iteration, batch in enumerate(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase						, __UpperCamelCase  :str    						=	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"
        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			)
        __UpperCamelCase  :str    						=	ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE			)			)]
    GradientState._reset_state()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=False			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	Accelerator(
        split_batches=SCREAMING_SNAKE_CASE						,    dispatch_batches=SCREAMING_SNAKE_CASE						,    gradient_accumulation_steps=2			)
    # Test that context manager behaves properly
    __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Dict    						=	get_training_setup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    for iteration, batch in enumerate(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase						, __UpperCamelCase  :List[str]    						=	batch.values()
        # Gather the distributed inputs and targs for the base model
        __UpperCamelCase						, __UpperCamelCase  :List[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"
        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"""
        __UpperCamelCase  :List[Any]    						=	(((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 lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	Accelerator()

    __UpperCamelCase  :List[str]    						=	RegressionDataset(length=80			)
    __UpperCamelCase  :int    						=	DataLoader(SCREAMING_SNAKE_CASE						,    batch_size=16			)
    __UpperCamelCase  :Any    						=	RegressionDataset(length=96			)
    __UpperCamelCase  :Tuple    						=	DataLoader(SCREAMING_SNAKE_CASE						,    batch_size=16			)
    __UpperCamelCase						, __UpperCamelCase  :Dict    						=	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 lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	Accelerator()
    __UpperCamelCase  :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(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 lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    main()


if __name__ == "__main__":
    main()






from __future__ import annotations

import bisect



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    if hi < 0:
        __UpperCamelCase  :str    						=	len(SCREAMING_SNAKE_CASE			)

    while lo < hi:
        __UpperCamelCase  :Optional[int]    						=	lo + (hi - lo) // 2
        if sorted_collection[mid] < item:
            __UpperCamelCase  :List[Any]    						=	mid + 1
        else:
            __UpperCamelCase  :Any    						=	mid

    return lo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    if hi < 0:
        __UpperCamelCase  :List[str]    						=	len(SCREAMING_SNAKE_CASE			)

    while lo < hi:
        __UpperCamelCase  :Optional[Any]    						=	lo + (hi - lo) // 2
        if sorted_collection[mid] <= item:
            __UpperCamelCase  :Dict    						=	mid + 1
        else:
            __UpperCamelCase  :Optional[Any]    						=	mid

    return lo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    sorted_collection.insert(bisect_left(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)						,    SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    sorted_collection.insert(bisect_right(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)						,    SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	0
    __UpperCamelCase  :List[str]    						=	len(SCREAMING_SNAKE_CASE			) - 1

    while left <= right:
        __UpperCamelCase  :Optional[int]    						=	left + (right - left) // 2
        __UpperCamelCase  :List[str]    						=	sorted_collection[midpoint]
        if current_item == item:
            return midpoint
        elif item < current_item:
            __UpperCamelCase  :Union[str, Any]    						=	midpoint - 1
        else:
            __UpperCamelCase  :Dict    						=	midpoint + 1
    return None



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	bisect.bisect_left(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    if index != len(SCREAMING_SNAKE_CASE			) and sorted_collection[index] == item:
        return index
    return None



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    if right < left:
        return None

    __UpperCamelCase  :str    						=	left + (right - left) // 2

    if sorted_collection[midpoint] == item:
        return midpoint
    elif sorted_collection[midpoint] > item:
        return binary_search_by_recursion(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    midpoint - 1			)
    else:
        return binary_search_by_recursion(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    midpoint + 1						,    SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
    __lowercase											=      input('''Enter numbers separated by comma:\n''').strip()
    __lowercase											=      sorted(int(item) for item in user_input.split(''','''))
    __lowercase											=      int(input('''Enter a single number to be found in the list:\n'''))
    __lowercase											=      binary_search(collection, target)
    if result is None:
        print(F'{target} was not found in {collection}.')
    else:
        print(F'{target} was found at position {result} in {collection}.')





