


'''simple docstring'''

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType


class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase		):
										__lowerCAmelCase							       =				data




				def __iter__(self		):
										for element in self.data:
																yield element

def 							__magic_name__(						lowerCamelCase=True):
						__lowerCAmelCase							       =				Accelerator(even_batches=lowerCamelCase)
						assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
						return accelerator

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase = False):
						if iterable:
												__lowerCAmelCase							       =				DummyIterableDataset(torch.as_tensor(range(lowerCamelCase)))
						else:
												__lowerCAmelCase							       =				TensorDataset(torch.as_tensor(range(lowerCamelCase)))

						__lowerCAmelCase							       =				DataLoader(lowerCamelCase,				batch_size=lowerCamelCase)
						__lowerCAmelCase							       =				accelerator.prepare(lowerCamelCase)

						return dl

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				):
						__lowerCAmelCase							       =				create_dataloader(accelerator=lowerCamelCase,				dataset_size=lowerCamelCase,				batch_size=lowerCamelCase)

						__lowerCAmelCase							       =				[len(batch[0]) for batch in dl]

						if accelerator.process_index == 0:
												assert batch_sizes == process_0_expected_batch_sizes
						elif accelerator.process_index == 1:
												assert batch_sizes == process_1_expected_batch_sizes

def 							__magic_name__(						):
						__lowerCAmelCase							       =				create_accelerator()

						# without padding, we would expect a different number of batches
						verify_dataloader_batch_sizes(
						    lowerCamelCase,				dataset_size=3,				batch_size=1,				process_0_expected_batch_sizes=[1, 1],				process_1_expected_batch_sizes=[1, 1],				)

						# without padding, we would expect the same number of batches, but different sizes
						verify_dataloader_batch_sizes(
						    lowerCamelCase,				dataset_size=7,				batch_size=2,				process_0_expected_batch_sizes=[2, 2],				process_1_expected_batch_sizes=[2, 2],				)

def 							__magic_name__(						):
						__lowerCAmelCase							       =				create_accelerator(even_batches=lowerCamelCase)

						verify_dataloader_batch_sizes(
						    lowerCamelCase,				dataset_size=3,				batch_size=1,				process_0_expected_batch_sizes=[1, 1],				process_1_expected_batch_sizes=[1],				)

						verify_dataloader_batch_sizes(
						    lowerCamelCase,				dataset_size=7,				batch_size=2,				process_0_expected_batch_sizes=[2, 2],				process_1_expected_batch_sizes=[2, 1],				)

def 							__magic_name__(						):
						__lowerCAmelCase							       =				create_accelerator(even_batches=lowerCamelCase)

						__lowerCAmelCase							       =				torch.nn.Linear(1,				1)
						__lowerCAmelCase							       =				accelerator.prepare(lowerCamelCase)

						__lowerCAmelCase							       =				create_dataloader(lowerCamelCase,				dataset_size=3,				batch_size=1)

						__lowerCAmelCase							       =				[]
						with accelerator.join_uneven_inputs([ddp_model]):
												for batch_idx, batch in enumerate(lowerCamelCase):
																		__lowerCAmelCase							       =				ddp_model(batch[0].float())
																		__lowerCAmelCase							       =				output.sum()
																		loss.backward()
																		batch_idxs.append(lowerCamelCase)

						accelerator.wait_for_everyone()

						if accelerator.process_index == 0:
												assert batch_idxs == [0, 1]
						elif accelerator.process_index == 1:
												assert batch_idxs == [0]

def 							__magic_name__(						lowerCamelCase):
						with warnings.catch_warnings(record=lowerCamelCase) as w:
												with accelerator.join_uneven_inputs([Mock()]):
																		pass

												assert issubclass(w[-1].category,				lowerCamelCase)
												assert "only supported for multi-GPU" in str(w[-1].message)

def 							__magic_name__(						):
						__lowerCAmelCase							       =				True
						__lowerCAmelCase							       =				False
						__lowerCAmelCase							       =				create_accelerator(even_batches=lowerCamelCase)
						__lowerCAmelCase							       =				torch.nn.Linear(1,				1)
						__lowerCAmelCase							       =				accelerator.prepare(lowerCamelCase)
						__lowerCAmelCase							       =				create_dataloader(lowerCamelCase,				dataset_size=3,				batch_size=1)
						__lowerCAmelCase							       =				create_dataloader(lowerCamelCase,				dataset_size=3,				batch_size=1)

						with accelerator.join_uneven_inputs([ddp_model],				even_batches=lowerCamelCase):
												__lowerCAmelCase							       =				train_dl.batch_sampler.even_batches
												__lowerCAmelCase							       =				valid_dl.batch_sampler.even_batches

						assert train_dl_overridden_value == overridden_even_batches
						assert valid_dl_overridden_value == overridden_even_batches
						assert train_dl.batch_sampler.even_batches == default_even_batches
						assert valid_dl.batch_sampler.even_batches == default_even_batches

def 							__magic_name__(						):
						__lowerCAmelCase							       =				True
						__lowerCAmelCase							       =				False
						__lowerCAmelCase							       =				create_accelerator(even_batches=lowerCamelCase)
						__lowerCAmelCase							       =				torch.nn.Linear(1,				1)
						__lowerCAmelCase							       =				accelerator.prepare(lowerCamelCase)
						create_dataloader(lowerCamelCase,				dataset_size=3,				batch_size=1,				iterable=lowerCamelCase)
						__lowerCAmelCase							       =				create_dataloader(lowerCamelCase,				dataset_size=3,				batch_size=1)

						with warnings.catch_warnings():
												warnings.filterwarnings('''ignore''')
												try:
																		with accelerator.join_uneven_inputs([ddp_model],				even_batches=lowerCamelCase):
																								__lowerCAmelCase							       =				batch_dl.batch_sampler.even_batches
												except AttributeError:
																		# ensure attribute error is not raised when processing iterable dl
																		raise AssertionError

						assert batch_dl_overridden_value == overridden_even_batches
						assert batch_dl.batch_sampler.even_batches == default_even_batches

def 							__magic_name__(						):
						__lowerCAmelCase							       =				create_accelerator()
						__lowerCAmelCase							       =				torch.nn.Linear(1,				1)
						__lowerCAmelCase							       =				accelerator.prepare(lowerCamelCase)
						create_dataloader(lowerCamelCase,				dataset_size=3,				batch_size=1,				iterable=lowerCamelCase)

						with warnings.catch_warnings(record=lowerCamelCase) as w:
												with accelerator.join_uneven_inputs([ddp_model],				even_batches=lowerCamelCase):
																		pass

												assert issubclass(w[-1].category,				lowerCamelCase)
												assert "only supported for map-style datasets" in str(w[-1].message)

def 							__magic_name__(						):
						__lowerCAmelCase							       =				create_accelerator()

						accelerator.print('''Test that even_batches variable ensures uniform batches across processes''')
						test_default_ensures_even_batch_sizes()

						accelerator.print('''Run tests with even_batches disabled''')
						test_can_disable_even_batches()

						accelerator.print('''Test joining uneven inputs''')
						test_can_join_uneven_inputs()

						accelerator.print('''Test overriding even_batches when joining uneven inputs''')
						test_join_can_override_even_batches()

						accelerator.print('''Test overriding even_batches for mixed dataloader types''')
						test_join_can_override_for_mixed_type_dataloaders()

						accelerator.print('''Test overriding even_batches raises a warning for iterable dataloaders''')
						test_join_raises_warning_for_iterable_when_overriding_even_batches()

						accelerator.print('''Test join with non DDP distributed raises warning''')
						__lowerCAmelCase							       =				accelerator.state.distributed_type
						__lowerCAmelCase							       =				DistributedType.FSDP
						test_join_raises_warning_for_non_ddp_distributed(lowerCamelCase)
						__lowerCAmelCase							       =				original_state


if __name__ == "__main__":
	main()





'''simple docstring'''

from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

