






import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def 					_lowerCamelCase(			lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		str      ):





				'''simple docstring'''







				assert isinstance(lowerCAmelCase__    ,    lowerCAmelCase__      )
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('keep_in_memory'    ,    [False, True]      )
def 					_lowerCamelCase(			lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Tuple      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Any													=   tmp_path / 'cache'
				SCREAMING_SNAKE_CASE_   :	Tuple													=   {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								SCREAMING_SNAKE_CASE_   :	Tuple													=   JsonDatasetReader(lowerCAmelCase__    ,    cache_dir=lowerCAmelCase__    ,    keep_in_memory=lowerCAmelCase__      ).read()
				_check_json_dataset(lowerCAmelCase__    ,    lowerCAmelCase__      )



@pytest.mark.parametrize(
    'features'    ,    [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]    ,    )
def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Tuple													=   tmp_path / 'cache'
				SCREAMING_SNAKE_CASE_   :	Tuple													=   {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   features.copy() if features else default_expected_features
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   (
				    Features({feature: Value(lowerCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
				)
				SCREAMING_SNAKE_CASE_   :	List[str]													=   JsonDatasetReader(lowerCAmelCase__    ,    features=lowerCAmelCase__    ,    cache_dir=lowerCAmelCase__      ).read()
				_check_json_dataset(lowerCAmelCase__    ,    lowerCAmelCase__      )



@pytest.mark.parametrize(
    'features'    ,    [
        None,
        {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
    ]    ,    )
def 					_lowerCamelCase(			lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		Optional[int]    ,    lowerCAmelCase__				:		str      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[str]													=   tmp_path / 'cache'
				SCREAMING_SNAKE_CASE_   :	int													=   {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'}
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   features.copy() if features else default_expected_features
				SCREAMING_SNAKE_CASE_   :	Tuple													=   (
				    Features({feature: Value(lowerCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
				)
				SCREAMING_SNAKE_CASE_   :	Any													=   JsonDatasetReader(lowerCAmelCase__    ,    features=lowerCAmelCase__    ,    cache_dir=lowerCAmelCase__      ).read()
				assert isinstance(lowerCAmelCase__    ,    lowerCAmelCase__      )
				assert dataset.num_rows == 2
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_3", "col_1", "col_2"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[Any]													=   {'col_2': 'int64', 'col_3': 'float64', 'col_1': 'string'}
				SCREAMING_SNAKE_CASE_   :	List[str]													=   features.copy()
				SCREAMING_SNAKE_CASE_   :	Any													=   (
				    Features({feature: Value(lowerCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
				)
				SCREAMING_SNAKE_CASE_   :	str													=   tmp_path / 'cache'
				SCREAMING_SNAKE_CASE_   :	Tuple													=   JsonDatasetReader(lowerCAmelCase__    ,    features=lowerCAmelCase__    ,    cache_dir=lowerCAmelCase__      ).read()
				assert isinstance(lowerCAmelCase__    ,    lowerCAmelCase__      )
				assert dataset.num_rows == 2
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_2", "col_3", "col_1"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('split'    ,    [None, NamedSplit('train'      ), 'train', 'test']      )
def 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		Union[str, Any]    ,    lowerCAmelCase__				:		Tuple      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   tmp_path / 'cache'
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   JsonDatasetReader(lowerCAmelCase__    ,    cache_dir=lowerCAmelCase__    ,    split=lowerCAmelCase__      ).read()
				_check_json_dataset(lowerCAmelCase__    ,    lowerCAmelCase__      )
				assert dataset.split == split if split else "train"



@pytest.mark.parametrize('path_type'    ,    [str, list]      )
def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







				if issubclass(lowerCAmelCase__    ,    lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Any													=   jsonl_path
				elif issubclass(lowerCAmelCase__    ,    lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	List[str]													=   [jsonl_path]
				SCREAMING_SNAKE_CASE_   :	str													=   tmp_path / 'cache'
				SCREAMING_SNAKE_CASE_   :	Tuple													=   {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   JsonDatasetReader(lowerCAmelCase__    ,    cache_dir=lowerCAmelCase__      ).read()
				_check_json_dataset(lowerCAmelCase__    ,    lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		Any=("train",)      ):





				'''simple docstring'''







				assert isinstance(lowerCAmelCase__    ,    lowerCAmelCase__      )
				for split in splits:
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   dataset_dict[split]
								assert dataset.num_rows == 4
								assert dataset.num_columns == 3
								assert dataset.column_names == ["col_1", "col_2", "col_3"]
								for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('keep_in_memory'    ,    [False, True]      )
def 					_lowerCamelCase(			lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   tmp_path / 'cache'
				SCREAMING_SNAKE_CASE_   :	Tuple													=   {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   JsonDatasetReader({'train': jsonl_path}    ,    cache_dir=lowerCAmelCase__    ,    keep_in_memory=lowerCAmelCase__      ).read()
				_check_json_datasetdict(lowerCAmelCase__    ,    lowerCAmelCase__      )



@pytest.mark.parametrize(
    'features'    ,    [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]    ,    )
def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   tmp_path / 'cache'
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				SCREAMING_SNAKE_CASE_   :	Dict													=   features.copy() if features else default_expected_features
				SCREAMING_SNAKE_CASE_   :	Any													=   (
				    Features({feature: Value(lowerCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
				)
				SCREAMING_SNAKE_CASE_   :	str													=   JsonDatasetReader({'train': jsonl_path}    ,    features=lowerCAmelCase__    ,    cache_dir=lowerCAmelCase__      ).read()
				_check_json_datasetdict(lowerCAmelCase__    ,    lowerCAmelCase__      )



@pytest.mark.parametrize('split'    ,    [None, NamedSplit('train'      ), 'train', 'test']      )
def 					_lowerCamelCase(			lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		str      ):





				'''simple docstring'''







				if split:
								SCREAMING_SNAKE_CASE_   :	str													=   {split: jsonl_path}
				else:
								SCREAMING_SNAKE_CASE_   :	List[str]													=   'train'
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {'train': jsonl_path, 'test': jsonl_path}
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tmp_path / 'cache'
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
				SCREAMING_SNAKE_CASE_   :	Tuple													=   JsonDatasetReader(lowerCAmelCase__    ,    cache_dir=lowerCAmelCase__      ).read()
				_check_json_datasetdict(lowerCAmelCase__    ,    lowerCAmelCase__    ,    splits=list(path.keys()      )      )
				assert all(dataset[split].split == split for split in path.keys()      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		Union[str, Any]      ):





				'''simple docstring'''







				return json.load(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return [json.loads(lowerCAmelCase__      ) for line in buffer]


class 							__a							:





		'''simple docstring'''



		@pytest.mark.parametrize('lines, load_json_function' ,    [(True, load_json_lines), (False, load_json)]       )
		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						with io.BytesIO() as buffer:
										JsonDatasetWriter(UpperCamelCase__ ,    UpperCamelCase__ ,    lines=UpperCamelCase__       ).write()
										buffer.seek(0       )
										SCREAMING_SNAKE_CASE_   :	Any													=   load_json_function(UpperCamelCase__       )
						assert isinstance(UpperCamelCase__ ,    UpperCamelCase__       )
						assert isinstance(exported_content[0] ,    UpperCamelCase__       )
						assert len(UpperCamelCase__       ) == 10


		@pytest.mark.parametrize(
		    'orient, container, keys, len_at' ,    [
		        ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
		        ('split', dict, {'columns', 'data'}, 'data'),
		        ('index', dict, set('0123456789'       ), None),
		        ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
		        ('values', list, None, None),
		        ('table', dict, {'schema', 'data'}, 'data'),
		    ] ,    )
		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						with io.BytesIO() as buffer:
										JsonDatasetWriter(UpperCamelCase__ ,    UpperCamelCase__ ,    lines=UpperCamelCase__ ,    orient=UpperCamelCase__       ).write()
										buffer.seek(0       )
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   load_json(UpperCamelCase__       )
						assert isinstance(UpperCamelCase__ ,    UpperCamelCase__       )
						if keys:
										if container is dict:
														assert exported_content.keys() == keys
										else:
														assert exported_content[0].keys() == keys
						else:
										assert not hasattr(UpperCamelCase__ ,    'keys'       ) and not hasattr(exported_content[0] ,    'keys'       )
						if len_at:
										assert len(exported_content[len_at]       ) == 10
						else:
										assert len(UpperCamelCase__       ) == 10


		@pytest.mark.parametrize('lines, load_json_function' ,    [(True, load_json_lines), (False, load_json)]       )
		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						with io.BytesIO() as buffer:
										JsonDatasetWriter(UpperCamelCase__ ,    UpperCamelCase__ ,    lines=UpperCamelCase__ ,    num_proc=2       ).write()
										buffer.seek(0       )
										SCREAMING_SNAKE_CASE_   :	Any													=   load_json_function(UpperCamelCase__       )
						assert isinstance(UpperCamelCase__ ,    UpperCamelCase__       )
						assert isinstance(exported_content[0] ,    UpperCamelCase__       )
						assert len(UpperCamelCase__       ) == 10


		@pytest.mark.parametrize(
		    'orient, container, keys, len_at' ,    [
		        ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
		        ('split', dict, {'columns', 'data'}, 'data'),
		        ('index', dict, set('0123456789'       ), None),
		        ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
		        ('values', list, None, None),
		        ('table', dict, {'schema', 'data'}, 'data'),
		    ] ,    )
		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						with io.BytesIO() as buffer:
										JsonDatasetWriter(UpperCamelCase__ ,    UpperCamelCase__ ,    lines=UpperCamelCase__ ,    orient=UpperCamelCase__ ,    num_proc=2       ).write()
										buffer.seek(0       )
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   load_json(UpperCamelCase__       )
						assert isinstance(UpperCamelCase__ ,    UpperCamelCase__       )
						if keys:
										if container is dict:
														assert exported_content.keys() == keys
										else:
														assert exported_content[0].keys() == keys
						else:
										assert not hasattr(UpperCamelCase__ ,    'keys'       ) and not hasattr(exported_content[0] ,    'keys'       )
						if len_at:
										assert len(exported_content[len_at]       ) == 10
						else:
										assert len(UpperCamelCase__       ) == 10


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						with pytest.raises(UpperCamelCase__       ):
										with io.BytesIO() as buffer:
														JsonDatasetWriter(UpperCamelCase__ ,    UpperCamelCase__ ,    num_proc=0       )






		@pytest.mark.parametrize('compression, extension' ,    [('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')]       )
		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tmp_path_factory.mktemp('data'       ) / F'''test.json.{extension}'''
						SCREAMING_SNAKE_CASE_   :	Tuple													=   str(shared_datadir / F'''test_file.json.{extension}'''       )
						JsonDatasetWriter(UpperCamelCase__ ,    UpperCamelCase__ ,    compression=UpperCamelCase__       ).write()

						with fsspec.open(UpperCamelCase__ ,    'rb' ,    compression='infer'       ) as f:
										SCREAMING_SNAKE_CASE_   :	int													=   f.read()
						with fsspec.open(UpperCamelCase__ ,    'rb' ,    compression='infer'       ) as f:
										SCREAMING_SNAKE_CASE_   :	List[str]													=   f.read()
						assert exported_content == original_content







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(			lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		Optional[int]      ):





				'''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(			lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Optional[int]    ,    lowerCAmelCase__				:		Optional[Any]=True      ):





				'''simple docstring'''







				model.train()
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   model(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   F.mse_loss(lowerCAmelCase__    ,    target.to(output.device      )      )
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		Union[str, Any]    ,    lowerCAmelCase__				:		List[str]=False      ):





				'''simple docstring'''







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



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







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

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



def 					_lowerCamelCase(			lowerCAmelCase__				:		Any      ):





				'''simple docstring'''







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

								# 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      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   ddp_input[torch.randperm(len(lowerCAmelCase__      )      )]



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]=False    ,    lowerCAmelCase__				:		Union[str, Any]=False      ):





				'''simple docstring'''







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

								# 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(lowerCAmelCase__      ) - 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      )
								SCREAMING_SNAKE_CASE_   :	str													=   ddp_input[torch.randperm(len(lowerCAmelCase__      )      )]
				GradientState._reset_state()



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





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   Accelerator(
				    split_batches=lowerCAmelCase__    ,    dispatch_batches=lowerCAmelCase__    ,    gradient_accumulation_steps=2      )
				# Test that context manager behaves properly
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   get_training_setup(lowerCAmelCase__    ,    lowerCAmelCase__      )
				for iteration, batch in enumerate(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[Any]													=   batch.values()
								# Gather the distributed inputs and targs for the base model
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   accelerator.gather((ddp_input, ddp_target)      )
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	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(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								opt.step()

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



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   Accelerator()

				SCREAMING_SNAKE_CASE_   :	int													=   RegressionDataset(length=80      )
				SCREAMING_SNAKE_CASE_   :	str													=   DataLoader(lowerCAmelCase__    ,    batch_size=16      )
				SCREAMING_SNAKE_CASE_   :	Dict													=   RegressionDataset(length=96      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   DataLoader(lowerCAmelCase__    ,    batch_size=16      )
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   accelerator.prepare(lowerCAmelCase__    ,    lowerCAmelCase__      )
				assert accelerator.gradient_state.active_dataloader is None
				for iteration, _ in enumerate(lowerCAmelCase__      ):
								assert id(accelerator.gradient_state.active_dataloader      ) == id(lowerCAmelCase__      )
								if iteration < len(lowerCAmelCase__      ) - 1:
												assert not accelerator.gradient_state.end_of_dataloader
												if iteration == 1:
																for batch_num, _ in enumerate(lowerCAmelCase__      ):
																				assert id(accelerator.gradient_state.active_dataloader      ) == id(lowerCAmelCase__      )
																				if batch_num < len(lowerCAmelCase__      ) - 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'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   Accelerator()
				SCREAMING_SNAKE_CASE_   :	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(lowerCAmelCase__      )
				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(lowerCAmelCase__      )
				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(lowerCAmelCase__    ,    lowerCAmelCase__      )

    # 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(lowerCAmelCase__    ,    lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[int]      ):





				'''simple docstring'''







				main()


if __name__ == "__main__":
				main()