


"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_       							=    logging.get_logger(__name__)

a_       							=    {
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}
class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			'data2vec-text'







		def __init__(					self      :       str    ,   a__      :       Optional[int]=3_05_22    ,   a__      :       Any=7_68    ,   a__      :       int=12    ,   a__      :       List[Any]=12    ,   a__      :       int=30_72    ,   a__      :       Tuple="gelu"    ,   a__      :       int=0.1    ,   a__      :       List[Any]=0.1    ,   a__      :       List[Any]=5_12    ,   a__      :       Optional[Any]=2    ,   a__      :       str=0.0_2    ,   a__      :       Union[str, Any]=1E-1_2    ,   a__      :       str=1    ,   a__      :       Dict=0    ,   a__      :       Optional[int]=2    ,   a__      :       List[str]="absolute"    ,   a__      :       Optional[Any]=True    ,   a__      :       Any=None    ,   **a__      :       Optional[int]    ,   )       ->						Dict:





						'''simple docstring'''







						super().__init__(pad_token_id=a__    ,   bos_token_id=a__    ,   eos_token_id=a__    ,   **a__		)

						_A					  =			vocab_size
						_A					  =			hidden_size
						_A					  =			num_hidden_layers
						_A					  =			num_attention_heads
						_A					  =			hidden_act
						_A					  =			intermediate_size
						_A					  =			hidden_dropout_prob
						_A					  =			attention_probs_dropout_prob
						_A					  =			max_position_embeddings
						_A					  =			type_vocab_size
						_A					  =			initializer_range
						_A					  =			layer_norm_eps
						_A					  =			position_embedding_type
						_A					  =			use_cache
						_A					  =			classifier_dropout





class snake_case      (  _UpperCamelCase):







		@property
		def  a_ (					self      :       Optional[int]		)       ->						Mapping[str, Mapping[int, str]]:





						'''simple docstring'''







						if self.task == "multiple-choice":
										_A					  =			{0: "batch", 1: "choice", 2: "sequence"}
						else:
										_A					  =			{0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]		)



"""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 snake_case      (  _UpperCamelCase):







		def __init__(					self      :       List[Any]    ,   a__      :       Any		)       ->						Any:





						'''simple docstring'''







						_A					  =			data







		def __iter__(					self      :       List[str]		)       ->						str:





						'''simple docstring'''







						for element in self.data:
										yield element






def      a__ (				__lowercase=True   )	->	Tuple:
				_A					  =			Accelerator(even_batches=__lowercase   )
				assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
				return accelerator






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase = False   )	->	Union[str, Any]:
				if iterable:
								_A					  =			DummyIterableDataset(torch.as_tensor(range(__lowercase   )   )   )
				else:
								_A					  =			TensorDataset(torch.as_tensor(range(__lowercase   )   )   )

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

				return dl






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			)	->	Dict:
				_A					  =			create_dataloader(accelerator=__lowercase     ,			dataset_size=__lowercase     ,			batch_size=__lowercase   )

				_A					  =			[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      a__ (				)	->	List[str]:
				_A					  =			create_accelerator()

				# without padding, we would expect a different number of batches
				verify_dataloader_batch_sizes(
				    __lowercase     ,			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(
				    __lowercase     ,			dataset_size=7     ,			batch_size=2     ,			process_0_expected_batch_sizes=[2, 2]     ,			process_1_expected_batch_sizes=[2, 2]     ,			)






def      a__ (				)	->	List[Any]:
				_A					  =			create_accelerator(even_batches=__lowercase   )

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

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






def      a__ (				)	->	int:
				_A					  =			create_accelerator(even_batches=__lowercase   )

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

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

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

				accelerator.wait_for_everyone()

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






def      a__ (				__lowercase   )	->	List[str]:
				with warnings.catch_warnings(record=__lowercase   ) as w:
								with accelerator.join_uneven_inputs([Mock()]   ):
												pass

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






def      a__ (				)	->	Tuple:
				_A					  =			True
				_A					  =			False
				_A					  =			create_accelerator(even_batches=__lowercase   )
				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )
				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )
				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )

				with accelerator.join_uneven_inputs([ddp_model]     ,			even_batches=__lowercase   ):
								_A					  =			train_dl.batch_sampler.even_batches
								_A					  =			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      a__ (				)	->	int:
				_A					  =			True
				_A					  =			False
				_A					  =			create_accelerator(even_batches=__lowercase   )
				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )
				create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1     ,			iterable=__lowercase   )
				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )

				with warnings.catch_warnings():
								warnings.filterwarnings("ignore"   )
								try:
												with accelerator.join_uneven_inputs([ddp_model]     ,			even_batches=__lowercase   ):
																_A					  =			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      a__ (				)	->	Optional[Any]:
				_A					  =			create_accelerator()
				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )
				create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1     ,			iterable=__lowercase   )

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

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






def      a__ (				)	->	Optional[Any]:
				_A					  =			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"   )
				_A					  =			accelerator.state.distributed_type
				_A					  =			DistributedType.FSDP
				test_join_raises_warning_for_non_ddp_distributed(__lowercase   )
				_A					  =			original_state


if __name__ == "__main__":
		main()