
"""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    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	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    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase=True		):
	model.train()
	__lowerCAmelCase      :						Optional[int]						      =				model(_UpperCamelCase		)
	__lowerCAmelCase      :						List[str]						      =				F.mse_loss(_UpperCamelCase	,  target.to(output.device		)		)
	if not do_backward:
		loss /= accelerator.gradient_accumulation_steps
		loss.backward()
	else:
		accelerator.backward(_UpperCamelCase		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase=False		):
	set_seed(42		)
	__lowerCAmelCase      :						Optional[Any]						      =				RegressionModel()
	__lowerCAmelCase      :						str						      =				deepcopy(_UpperCamelCase		)
	__lowerCAmelCase      :						str						      =				RegressionDataset(length=80		)
	__lowerCAmelCase      :						Tuple						      =				DataLoader(_UpperCamelCase	,  batch_size=16		)
	model.to(accelerator.device		)
	if sched:
		__lowerCAmelCase      :						Union[str, Any]						      =				AdamW(params=model.parameters()	,  lr=1e-3		)
		__lowerCAmelCase      :						List[Any]						      =				AdamW(params=ddp_model.parameters()	,  lr=1e-3		)
		__lowerCAmelCase      :						Dict						      =				LambdaLR(_UpperCamelCase	,  lr_lambda=lambda _UpperCamelCase		: epoch**0.65		)
		__lowerCAmelCase      :						Tuple						      =				LambdaLR(_UpperCamelCase	,  lr_lambda=lambda _UpperCamelCase		: epoch**0.65		)
	# Make a copy of `model`
	if sched:
		__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						int						      =				accelerator.prepare(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)
	else:
		__lowerCAmelCase      ,       __lowerCAmelCase      :						str						      =				accelerator.prepare(_UpperCamelCase	,  _UpperCamelCase		)
	if sched:
		return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
	return model, ddp_model, dataloader
def    __lowerCAmelCase    (_UpperCamelCase		):
	# Test when on a single CPU or GPU that the context manager does nothing
	__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						Any						      =				get_training_setup(_UpperCamelCase		)
	# Use a single batch
	__lowerCAmelCase      ,       __lowerCAmelCase      :						str						      =				next(iter(_UpperCamelCase		)		).values()
	for iteration in range(3		):
		# Gather the distributed inputs and targs for the base model
		__lowerCAmelCase      ,       __lowerCAmelCase      :						List[Any]						      =				accelerator.gather((ddp_input, ddp_target)		)
		__lowerCAmelCase      ,       __lowerCAmelCase      :						int						      =				input.to(accelerator.device		), target.to(accelerator.device		)
		# Perform our initial ground truth step in non "DDP"
		step_model(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)
		# Do "gradient accumulation" (noop)
		if iteration % 2 == 0:
			# Accumulate grads locally
			with accelerator.no_sync(_UpperCamelCase		):
				step_model(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)
		else:
			# Sync grads
			step_model(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)

		# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
		check_model_parameters(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)
		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		)
		__lowerCAmelCase      :						Union[str, Any]						      =				ddp_input[torch.randperm(len(_UpperCamelCase		)		)]
def    __lowerCAmelCase    (_UpperCamelCase		):
	# Test on distributed setup that context manager behaves properly
	__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						Tuple						      =				get_training_setup(_UpperCamelCase		)
	# Use a single batch
	__lowerCAmelCase      ,       __lowerCAmelCase      :						Union[str, Any]						      =				next(iter(_UpperCamelCase		)		).values()
	for iteration in range(3		):
		# Gather the distributed inputs and targs for the base model
		__lowerCAmelCase      ,       __lowerCAmelCase      :						List[Any]						      =				accelerator.gather((ddp_input, ddp_target)		)
		__lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[int]						      =				input.to(accelerator.device		), target.to(accelerator.device		)
		# Perform our initial ground truth step in non "DDP"
		step_model(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)
		# Do "gradient accumulation" (noop)
		if iteration % 2 == 0:
			# Accumulate grads locally
			with accelerator.no_sync(_UpperCamelCase		):
				step_model(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)
		else:
			# Sync grads
			step_model(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)

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

		# 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(_UpperCamelCase		) - 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		)
		__lowerCAmelCase      :						Dict						      =				ddp_input[torch.randperm(len(_UpperCamelCase		)		)]
	GradientState._reset_state()
def    __lowerCAmelCase    (_UpperCamelCase=False	,  _UpperCamelCase=False		):
	__lowerCAmelCase      :						Tuple						      =				Accelerator(
	    split_batches=_UpperCamelCase	,  dispatch_batches=_UpperCamelCase	,  gradient_accumulation_steps=2		)
	# Test that context manager behaves properly
	__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						int						      =				get_training_setup(_UpperCamelCase	,  _UpperCamelCase		)
	for iteration, batch in enumerate(_UpperCamelCase		):
		__lowerCAmelCase      ,       __lowerCAmelCase      :						str						      =				batch.values()
		# Gather the distributed inputs and targs for the base model
		__lowerCAmelCase      ,       __lowerCAmelCase      :						str						      =				accelerator.gather((ddp_input, ddp_target)		)
		__lowerCAmelCase      ,       __lowerCAmelCase      :						Union[str, 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(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)
		opt.step()

		if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_UpperCamelCase		)):
			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(_UpperCamelCase		):
			step_model(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)
			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"
		__lowerCAmelCase      :						Dict						      =				(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_UpperCamelCase		))
		if accelerator.num_processes > 1:
			check_model_parameters(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)
		# Shuffle ddp_input on each iteration
		torch.manual_seed(1337 + iteration		)
	GradientState._reset_state()
def    __lowerCAmelCase    ():
	__lowerCAmelCase      :						Union[str, Any]						      =				Accelerator()

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

    # 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(_UpperCamelCase	,  _UpperCamelCase		)
def    __lowerCAmelCase    (_UpperCamelCase		):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
							main()

"""simple docstring"""







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate




@dataclass(frozen=_lowerCamelCase)
class 			A__	(     _lowerCamelCase):
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		A_						:	str           =     field(default='text-classification'	,					metadata={'include_in_asdict_even_if_is_default': True})
		A_						:	ClassVar[Features]           =     Features({'text': Value('string')})
		A_						:	ClassVar[Features]           =     Features({'labels': ClassLabel})
		A_						:	str           =     "text"
		A_						:	str           =     "labels"

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if self.label_column not in features:
				raise ValueError(f"Column {self.label_column} is not present in features."			)
			if not isinstance(features[self.label_column]	,		_SCREAMING_SNAKE_CASE			):
				raise ValueError(f"Column {self.label_column} is not a ClassLabel."			)
			__lowerCAmelCase      :						Any						      =				copy.deepcopy(self			)
			__lowerCAmelCase      :						Dict						      =				self.label_schema.copy()
			__lowerCAmelCase      :						List[Any]						      =				features[self.label_column]
			__lowerCAmelCase      :						Dict						      =				label_schema
			return task_template







		@property
		def 							__lowerCamelCase							(				self			):
			return {
			    self.text_column: "text",
			    self.label_column: "labels",
			}