





'''simple docstring'''



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 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							data


	def __iter__(self	)							->							Optional[Any]:







				'''simple docstring'''





				for element in self.data:
							yield element





def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]=True			):
			__lowercase			  =							Accelerator(even_batches=lowerCamelCase_			)
			assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
			return accelerator




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Accelerator						,    lowerCamelCase_     :					int						,    lowerCamelCase_     :					int						,    lowerCamelCase_     :					bool = False			):
			if iterable:
						__lowercase			  =							DummyIterableDataset(torch.as_tensor(range(lowerCamelCase_			)			)			)
			else:
						__lowercase			  =							TensorDataset(torch.as_tensor(range(lowerCamelCase_			)			)			)

			__lowercase			  =							DataLoader(lowerCamelCase_						,    batch_size=lowerCamelCase_			)
			__lowercase			  =							accelerator.prepare(lowerCamelCase_			)

			return dl




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Accelerator						,    lowerCamelCase_     :					int						,    lowerCamelCase_     :					int						,    lowerCamelCase_     :					List[int]						,    lowerCamelCase_     :					List[int]						,    ):
			__lowercase			  =							create_dataloader(accelerator=lowerCamelCase_						,    dataset_size=lowerCamelCase_						,    batch_size=lowerCamelCase_			)

			__lowercase			  =							[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 				_lowerCAmelCase  (			):
			__lowercase			  =							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 				_lowerCAmelCase  (			):
			__lowercase			  =							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 				_lowerCAmelCase  (			):
			__lowercase			  =							create_accelerator(even_batches=lowerCamelCase_			)

			__lowercase			  =							torch.nn.Linear(1						,    1			)
			__lowercase			  =							accelerator.prepare(lowerCamelCase_			)

			__lowercase			  =							create_dataloader(lowerCamelCase_						,    dataset_size=3						,    batch_size=1			)

			__lowercase			  =							[]
			with accelerator.join_uneven_inputs([ddp_model]			):
						for batch_idx, batch in enumerate(lowerCamelCase_			):
									__lowercase			  =							ddp_model(batch[0].float()			)
									__lowercase			  =							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 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]			):
			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 				_lowerCAmelCase  (			):
			__lowercase			  =							True
			__lowercase			  =							False
			__lowercase			  =							create_accelerator(even_batches=lowerCamelCase_			)
			__lowercase			  =							torch.nn.Linear(1						,    1			)
			__lowercase			  =							accelerator.prepare(lowerCamelCase_			)
			__lowercase			  =							create_dataloader(lowerCamelCase_						,    dataset_size=3						,    batch_size=1			)
			__lowercase			  =							create_dataloader(lowerCamelCase_						,    dataset_size=3						,    batch_size=1			)

			with accelerator.join_uneven_inputs([ddp_model]						,    even_batches=lowerCamelCase_			):
						__lowercase			  =							train_dl.batch_sampler.even_batches
						__lowercase			  =							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 				_lowerCAmelCase  (			):
			__lowercase			  =							True
			__lowercase			  =							False
			__lowercase			  =							create_accelerator(even_batches=lowerCamelCase_			)
			__lowercase			  =							torch.nn.Linear(1						,    1			)
			__lowercase			  =							accelerator.prepare(lowerCamelCase_			)
			create_dataloader(lowerCamelCase_						,    dataset_size=3						,    batch_size=1						,    iterable=lowerCamelCase_			)
			__lowercase			  =							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_			):
												__lowercase			  =							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 				_lowerCAmelCase  (			):
			__lowercase			  =							create_accelerator()
			__lowercase			  =							torch.nn.Linear(1						,    1			)
			__lowercase			  =							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 				_lowerCAmelCase  (			):
			__lowercase			  =							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'''			)
			__lowercase			  =							accelerator.state.distributed_type
			__lowercase			  =							DistributedType.FSDP
			test_join_raises_warning_for_non_ddp_distributed(lowerCamelCase_			)
			__lowercase			  =							original_state


if __name__ == "__main__":
	main()












'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Union[str, Any]     =    "gpt_neox_japanese"


	def __init__(self       ,_lowerCamelCase=32000       ,_lowerCamelCase=2560       ,_lowerCamelCase=32       ,_lowerCamelCase=32       ,_lowerCamelCase=4       ,_lowerCamelCase="gelu"       ,_lowerCamelCase=1.0_0       ,_lowerCamelCase=10000       ,_lowerCamelCase=2048       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-5       ,_lowerCamelCase=True       ,_lowerCamelCase=31996       ,_lowerCamelCase=31999       ,_lowerCamelCase=0.1       ,_lowerCamelCase=0.0       ,**_lowerCamelCase       ,)							->							Optional[int]:







				'''simple docstring'''





				super().__init__(bos_token_id=_lowerCamelCase       ,eos_token_id=_lowerCamelCase       ,**_lowerCamelCase	)
				__lowercase			  =							vocab_size
				__lowercase			  =							max_position_embeddings
				__lowercase			  =							hidden_size
				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_attention_heads
				__lowercase			  =							intermediate_multiple_size
				__lowercase			  =							hidden_act
				__lowercase			  =							rotary_pct
				__lowercase			  =							rotary_emb_base
				__lowercase			  =							initializer_range
				__lowercase			  =							layer_norm_eps
				__lowercase			  =							use_cache
				__lowercase			  =							attention_dropout
				__lowercase			  =							hidden_dropout





