






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_		(lowercase__						:       Any     ,					lowercase__						:       str     ,					lowercase__						:       List[Any]     ,					lowercase__						:       Tuple       )		->    Dict:


				'''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_		(lowercase__						:       Any     ,					lowercase__						:       Union[str, Any]     ,					lowercase__						:       str     ,					lowercase__						:       Union[str, Any]     ,					lowercase__						:       Tuple=True       )		->    Optional[int]:


				'''simple docstring'''




				model.train()
				lowerCAmelCase__      			=						model(lowercase__       )
				lowerCAmelCase__      			=						F.mse_loss(lowercase__     ,					target.to(output.device       )       )
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(lowercase__       )





def 	lowerCAmelCase_		(lowercase__						:       Union[str, Any]     ,					lowercase__						:       Optional[Any]=False       )		->    int:


				'''simple docstring'''




				set_seed(42       )
				lowerCAmelCase__      			=						RegressionModel()
				lowerCAmelCase__      			=						deepcopy(lowercase__       )
				lowerCAmelCase__      			=						RegressionDataset(length=80       )
				lowerCAmelCase__      			=						DataLoader(lowercase__     ,					batch_size=16       )
				model.to(accelerator.device       )
				if sched:
								lowerCAmelCase__      			=						AdamW(params=model.parameters()     ,					lr=1e-3       )
								lowerCAmelCase__      			=						AdamW(params=ddp_model.parameters()     ,					lr=1e-3       )
								lowerCAmelCase__      			=						LambdaLR(lowercase__     ,					lr_lambda=lambda lowercase__       : epoch**0.65       )
								lowerCAmelCase__      			=						LambdaLR(lowercase__     ,					lr_lambda=lambda lowercase__       : epoch**0.65       )
				# Make a copy of `model`
				if sched:
								lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.prepare(lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__       )
				else:
								lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.prepare(lowercase__     ,					lowercase__       )
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader





def 	lowerCAmelCase_		(lowercase__						:       str       )		->    Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						get_training_setup(lowercase__       )
				# Use a single batch
				lowerCAmelCase__			,		lowerCAmelCase__      			=						next(iter(lowercase__       )       ).values()
				for iteration in range(3       ):
								# Gather the distributed inputs and targs for the base model
								lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.gather((ddp_input, ddp_target)       )
								lowerCAmelCase__			,		lowerCAmelCase__      			=						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(13_37 + iteration       )
								lowerCAmelCase__      			=						ddp_input[torch.randperm(len(lowercase__       )       )]





def 	lowerCAmelCase_		(lowercase__						:       Optional[Any]       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						get_training_setup(lowercase__       )
				# Use a single batch
				lowerCAmelCase__			,		lowerCAmelCase__      			=						next(iter(lowercase__       )       ).values()
				for iteration in range(3       ):
								# Gather the distributed inputs and targs for the base model
								lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.gather((ddp_input, ddp_target)       )
								lowerCAmelCase__			,		lowerCAmelCase__      			=						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(13_37 + iteration       )
								lowerCAmelCase__      			=						ddp_input[torch.randperm(len(lowercase__       )       )]





def 	lowerCAmelCase_		(lowercase__						:       Union[str, Any]=False     ,					lowercase__						:       List[str]=False       )		->    List[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						Accelerator(
				    split_batches=lowercase__     ,					dispatch_batches=lowercase__     ,					gradient_accumulation_steps=2       )
				# Test that context manager behaves properly
				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						get_training_setup(lowercase__       )
				for iteration, batch in enumerate(lowercase__       ):
								lowerCAmelCase__			,		lowerCAmelCase__      			=						batch.values()
								# Gather the distributed inputs and targs for the base model
								lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.gather((ddp_input, ddp_target)       )
								lowerCAmelCase__			,		lowerCAmelCase__      			=						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(13_37 + iteration       )
								lowerCAmelCase__      			=						ddp_input[torch.randperm(len(lowercase__       )       )]
				GradientState._reset_state()





def 	lowerCAmelCase_		(lowercase__						:       List[Any]=False     ,					lowercase__						:       Optional[int]=False       )		->    Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						Accelerator(
				    split_batches=lowercase__     ,					dispatch_batches=lowercase__     ,					gradient_accumulation_steps=2       )
				# Test that context manager behaves properly
				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						get_training_setup(lowercase__     ,					lowercase__       )
				for iteration, batch in enumerate(lowercase__       ):
								lowerCAmelCase__			,		lowerCAmelCase__      			=						batch.values()
								# Gather the distributed inputs and targs for the base model
								lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.gather((ddp_input, ddp_target)       )
								lowerCAmelCase__			,		lowerCAmelCase__      			=						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'
								lowerCAmelCase__      			=						(((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(13_37 + iteration       )
				GradientState._reset_state()





def 	lowerCAmelCase_		()		->    Any:


				'''simple docstring'''




				lowerCAmelCase__      			=						Accelerator()

				lowerCAmelCase__      			=						RegressionDataset(length=80       )
				lowerCAmelCase__      			=						DataLoader(lowercase__     ,					batch_size=16       )
				lowerCAmelCase__      			=						RegressionDataset(length=96       )
				lowerCAmelCase__      			=						DataLoader(lowercase__     ,					batch_size=16       )
				lowerCAmelCase__			,		lowerCAmelCase__      			=						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 	lowerCAmelCase_		()		->    Tuple:


				'''simple docstring'''




				lowerCAmelCase__      			=						Accelerator()
				lowerCAmelCase__      			=						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 	lowerCAmelCase_		(lowercase__						:       Tuple       )		->    Any:


				'''simple docstring'''




				main()


if __name__ == "__main__":
			main()













import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


_UpperCAmelCase			:		List[Any]   				=    logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.'					)
@require_torch
@require_tf
@slow
class  lowerCAmelCase_       (				unittest.TestCase					):




							def 						__snake_case					(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Path ,							SCREAMING_SNAKE_CASE_     :   Union[str, None] = None ,							SCREAMING_SNAKE_CASE_     :   Union[List[str], None] = None ,							SCREAMING_SNAKE_CASE_     :   Union[str, List[str], None] = None ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							):
											lowerCAmelCase__      			=						[file for file in os.listdir(SCREAMING_SNAKE_CASE_	) if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)	)]

											if identifier is not None:
															lowerCAmelCase__      			=						[file for file in files if identifier in file]

											if n_identifier is not None:
															if isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
																			for n_ in n_identifier:
																							lowerCAmelCase__      			=						[file for file in files if n_ not in file]
															else:
																			lowerCAmelCase__      			=						[file for file in files if n_identifier not in file]

											lowerCAmelCase__      			=						ignore_files or []
											ignore_files.append('''__init__.py'''	)
											lowerCAmelCase__      			=						[file for file in files if file not in ignore_files]

											for file in files:
															# Open all files
															print('''Testing''' ,							SCREAMING_SNAKE_CASE_	)

															if only_modules:
																			lowerCAmelCase__      			=						file.split('''.'''	)[0]
																			try:
																							lowerCAmelCase__      			=						getattr(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)
																							lowerCAmelCase__      			=						doctest.DocTestSuite(SCREAMING_SNAKE_CASE_	)
																							lowerCAmelCase__      			=						unittest.TextTestRunner().run(SCREAMING_SNAKE_CASE_	)
																							self.assertIs(len(result.failures	) ,							0	)
																			except AttributeError:
																							logger.info(f'{module_identifier} is not a module.'	)
															else:
																			lowerCAmelCase__      			=						doctest.testfile(str('''..''' / directory / file	) ,							optionflags=doctest.ELLIPSIS	)
																			self.assertIs(result.failed ,							0	)




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						Path('''src/transformers'''	)
											lowerCAmelCase__      			=						'''modeling'''
											lowerCAmelCase__      			=						[
											    '''modeling_ctrl.py''',
											    '''modeling_tf_ctrl.py''',
											]
											self.analyze_directory(SCREAMING_SNAKE_CASE_ ,							identifier=SCREAMING_SNAKE_CASE_ ,							ignore_files=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Dict	):
											lowerCAmelCase__      			=						Path('''src/transformers'''	)
											lowerCAmelCase__      			=						'''tokenization'''
											self.analyze_directory(SCREAMING_SNAKE_CASE_ ,							identifier=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						Path('''src/transformers'''	)
											lowerCAmelCase__      			=						'''configuration'''
											self.analyze_directory(SCREAMING_SNAKE_CASE_ ,							identifier=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						Path('''src/transformers'''	)
											lowerCAmelCase__      			=						['''configuration''', '''modeling''', '''tokenization''']
											self.analyze_directory(SCREAMING_SNAKE_CASE_ ,							n_identifier=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						Path('''docs/source'''	)
											lowerCAmelCase__      			=						['''favicon.ico''']
											self.analyze_directory(SCREAMING_SNAKE_CASE_ ,							ignore_files=SCREAMING_SNAKE_CASE_ ,							only_modules=SCREAMING_SNAKE_CASE_	)





