



'''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      lowercase_		(      lowerCAmelCase__		:     int	,							lowerCAmelCase__		:     Optional[int]	,							lowerCAmelCase__		:     Union[str, Any]	,							lowerCAmelCase__		:     Tuple       ):

			"""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      lowercase_		(      lowerCAmelCase__		:     Optional[int]	,							lowerCAmelCase__		:     Union[str, Any]	,							lowerCAmelCase__		:     List[Any]	,							lowerCAmelCase__		:     List[Any]	,							lowerCAmelCase__		:     Tuple=True       ):

			"""simple docstring"""





			model.train()
			__UpperCAmelCase      :					str   =   model(lowercase_       )
			__UpperCAmelCase      :					Tuple   =   F.mse_loss(lowercase_	,							target.to(output.device       )       )
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(lowercase_       )


def      lowercase_		(      lowerCAmelCase__		:     Optional[int]	,							lowerCAmelCase__		:     List[str]=False       ):

			"""simple docstring"""





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


def      lowercase_		(      lowerCAmelCase__		:     str       ):

			"""simple docstring"""





			__UpperCAmelCase ,     __UpperCAmelCase ,     __UpperCAmelCase      :					int   =   get_training_setup(lowercase_       )
			# Use a single batch
			__UpperCAmelCase ,     __UpperCAmelCase      :					Union[str, Any]   =   next(iter(lowercase_       )       ).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      :					List[str]   =   input.to(accelerator.device       ), target.to(accelerator.device       )
						# Perform our initial ground truth step in non "DDP"
						step_model(lowercase_	,							lowercase_	,							lowercase_	,							lowercase_       )
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(lowercase_       ):
												step_model(lowercase_	,							lowercase_	,							lowercase_	,							lowercase_       )
						else:
									# Sync grads
									step_model(lowercase_	,							lowercase_	,							lowercase_	,							lowercase_       )

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


def      lowercase_		(      lowerCAmelCase__		:     List[Any]       ):

			"""simple docstring"""





			__UpperCAmelCase ,     __UpperCAmelCase ,     __UpperCAmelCase      :					List[Any]   =   get_training_setup(lowercase_       )
			# Use a single batch
			__UpperCAmelCase ,     __UpperCAmelCase      :					Optional[Any]   =   next(iter(lowercase_       )       ).values()
			for iteration in range(3       ):
						# Gather the distributed inputs and targs for the base model
						__UpperCAmelCase ,     __UpperCAmelCase      :					str   =   accelerator.gather((ddp_input, ddp_target)       )
						__UpperCAmelCase ,     __UpperCAmelCase      :					List[str]   =   input.to(accelerator.device       ), target.to(accelerator.device       )
						# Perform our initial ground truth step in non "DDP"
						step_model(lowercase_	,							lowercase_	,							lowercase_	,							lowercase_       )
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(lowercase_       ):
												step_model(lowercase_	,							lowercase_	,							lowercase_	,							lowercase_       )
						else:
									# Sync grads
									step_model(lowercase_	,							lowercase_	,							lowercase_	,							lowercase_       )

						# 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      :					List[str]   =   ddp_input[torch.randperm(len(lowercase_       )       )]


def      lowercase_		(      lowerCAmelCase__		:     Union[str, Any]=False	,							lowerCAmelCase__		:     Tuple=False       ):

			"""simple docstring"""





			__UpperCAmelCase      :					Optional[Any]   =   Accelerator(
			    split_batches=lowercase_	,							dispatch_batches=lowercase_	,							gradient_accumulation_steps=2       )
			# Test that context manager behaves properly
			__UpperCAmelCase ,     __UpperCAmelCase ,     __UpperCAmelCase      :					str   =   get_training_setup(lowercase_       )
			for iteration, batch in enumerate(lowercase_       ):
						__UpperCAmelCase ,     __UpperCAmelCase      :					List[Any]   =   batch.values()
						# Gather the distributed inputs and targs for the base model
						__UpperCAmelCase ,     __UpperCAmelCase      :					List[str]   =   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(lowercase_	,							lowercase_	,							lowercase_	,							lowercase_	,							lowercase_       )
						# Do "gradient accumulation" (noop)
						with accelerator.accumulate(lowercase_       ):
									step_model(lowercase_	,							lowercase_	,							lowercase_	,							lowercase_       )

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


def      lowercase_		(      lowerCAmelCase__		:     str=False	,							lowerCAmelCase__		:     Dict=False       ):

			"""simple docstring"""





			__UpperCAmelCase      :					List[Any]   =   Accelerator(
			    split_batches=lowercase_	,							dispatch_batches=lowercase_	,							gradient_accumulation_steps=2       )
			# Test that context manager behaves properly
			__UpperCAmelCase ,     __UpperCAmelCase ,     __UpperCAmelCase ,     __UpperCAmelCase ,     __UpperCAmelCase ,     __UpperCAmelCase ,     __UpperCAmelCase      :					List[Any]   =   get_training_setup(lowercase_	,							lowercase_       )
			for iteration, batch in enumerate(lowercase_       ):
						__UpperCAmelCase ,     __UpperCAmelCase      :					Dict   =   batch.values()
						# 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"
						model.train()
						ddp_model.train()
						step_model(lowercase_	,							lowercase_	,							lowercase_	,							lowercase_	,							lowercase_       )
						opt.step()

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


def      lowercase_		(      ):

			"""simple docstring"""





			__UpperCAmelCase      :					List[Any]   =   Accelerator()

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

			"""simple docstring"""





			__UpperCAmelCase      :					List[Any]   =   Accelerator()
			__UpperCAmelCase      :					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(lowercase_       )
			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(lowercase_       )
			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(lowercase_	,							lowercase_       )

    # 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(lowercase_	,							lowercase_       )


def      lowercase_		(      lowerCAmelCase__		:     str       ):

			"""simple docstring"""





			main()


if __name__ == "__main__":
					main()










import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       str:



       """simple docstring"""

       A__       =    BeautifulSoup(requests.get(lowercase_ ,				params=lowercase_						).content ,				'''html.parser'''						)
       A__       =    soup.find('''div''' ,				attrs={'''class''': '''gs_ri'''}						)
       A__       =    div.find('''div''' ,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
       return anchors[2].get_text()


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[Any]							       =   {
      """title""": (
          """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
          """capacitance, volumetric capacitance, and energy density"""
      ),
      """journal""": """Chem. Mater.""",
      """volume""": 30,
      """pages""": """3979-3990""",
      """year""": 2018,
      """hl""": """en""",
  }
  print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))


