'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_lowercase						: Optional[int]						=						logging.get_logger(__name__)

_lowercase						: List[Any]						=						{
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       '''beit'''







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  str=8192				,			lowercase_				:  Optional[Any]=768				,			lowercase_				:  List[str]=12				,			lowercase_				:  List[Any]=12				,			lowercase_				:  Dict=3072				,			lowercase_				:  str="gelu"				,			lowercase_				:  List[str]=0.0				,			lowercase_				:  str=0.0				,			lowercase_				:  List[Any]=0.02				,			lowercase_				:  List[Any]=1E-12				,			lowercase_				:  Dict=224				,			lowercase_				:  Union[str, Any]=16				,			lowercase_				:  Dict=3				,			lowercase_				:  Any=False				,			lowercase_				:  List[str]=False				,			lowercase_				:  str=False				,			lowercase_				:  List[Any]=False				,			lowercase_				:  Optional[int]=0.1				,			lowercase_				:  Dict=0.1				,			lowercase_				:  Optional[int]=True				,			lowercase_				:  Tuple=[3, 5, 7, 11]				,			lowercase_				:  Union[str, Any]=[1, 2, 3, 6]				,			lowercase_				:  str=True				,			lowercase_				:  int=0.4				,			lowercase_				:  Optional[Any]=256				,			lowercase_				:  List[Any]=1				,			lowercase_				:  int=False				,			lowercase_				:  Union[str, Any]=255				,			**lowercase_				:  Any				,			):
										super().__init__(**lowercase_    )

										lowercase_					:			Dict										=       vocab_size
										lowercase_					:			int										=       hidden_size
										lowercase_					:			Tuple										=       num_hidden_layers
										lowercase_					:			Any										=       num_attention_heads
										lowercase_					:			Dict										=       intermediate_size
										lowercase_					:			str										=       hidden_act
										lowercase_					:			Union[str, Any]										=       hidden_dropout_prob
										lowercase_					:			Union[str, Any]										=       attention_probs_dropout_prob
										lowercase_					:			Tuple										=       initializer_range
										lowercase_					:			List[Any]										=       layer_norm_eps

										lowercase_					:			Dict										=       image_size
										lowercase_					:			List[Any]										=       patch_size
										lowercase_					:			Dict										=       num_channels
										lowercase_					:			str										=       use_mask_token
										lowercase_					:			Optional[Any]										=       use_absolute_position_embeddings
										lowercase_					:			List[str]										=       use_relative_position_bias
										lowercase_					:			Any										=       use_shared_relative_position_bias
										lowercase_					:			Union[str, Any]										=       layer_scale_init_value
										lowercase_					:			int										=       drop_path_rate
										lowercase_					:			Union[str, Any]										=       use_mean_pooling
										# decode head attributes (semantic segmentation)
										lowercase_					:			List[Any]										=       out_indices
										lowercase_					:			Dict										=       pool_scales
										# auxiliary head attributes (semantic segmentation)
										lowercase_					:			Any										=       use_auxiliary_head
										lowercase_					:			Dict										=       auxiliary_loss_weight
										lowercase_					:			str										=       auxiliary_channels
										lowercase_					:			Optional[Any]										=       auxiliary_num_convs
										lowercase_					:			Dict										=       auxiliary_concat_input
										lowercase_					:			int										=       semantic_loss_ignore_index





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       version.parse('''1.11''')







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										return OrderedDict(
										    [
										        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
										    ]    )







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										return 1E-4






'''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     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  str    ):
										lowercase_					:			int										=       data







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




def 							lowerCamelCase	(      UpperCAmelCase__       :					Any=True )			->      Any:
							lowercase_					:			Optional[int]										=       Accelerator(even_batches=UpperCAmelCase__ )
							assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
							return accelerator




def 							lowerCamelCase	(      UpperCAmelCase__       :					Accelerator      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					bool = False )			->      Optional[Any]:
							if iterable:
														lowercase_					:			Dict										=       DummyIterableDataset(torch.as_tensor(range(UpperCAmelCase__ ) ) )
							else:
														lowercase_					:			Union[str, Any]										=       TensorDataset(torch.as_tensor(range(UpperCAmelCase__ ) ) )

							lowercase_					:			Any										=       DataLoader(UpperCAmelCase__      ,      batch_size=UpperCAmelCase__ )
							lowercase_					:			Any										=       accelerator.prepare(UpperCAmelCase__ )

							return dl




def 							lowerCamelCase	(      UpperCAmelCase__       :					Accelerator      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					List[int]      ,      UpperCAmelCase__       :					List[int]      ,      )			->      int:
							lowercase_					:			List[str]										=       create_dataloader(accelerator=UpperCAmelCase__      ,      dataset_size=UpperCAmelCase__      ,      batch_size=UpperCAmelCase__ )

							lowercase_					:			Tuple										=       [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	(      )			->      int:
							lowercase_					:			Tuple										=       create_accelerator()

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




def 							lowerCamelCase	(      )			->      Optional[int]:
							lowercase_					:			Optional[int]										=       create_accelerator(even_batches=UpperCAmelCase__ )

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

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




def 							lowerCamelCase	(      )			->      List[str]:
							lowercase_					:			str										=       create_accelerator(even_batches=UpperCAmelCase__ )

							lowercase_					:			Dict										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			Optional[Any]										=       accelerator.prepare(UpperCAmelCase__ )

							lowercase_					:			List[Any]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )

							lowercase_					:			Optional[Any]										=       []
							with accelerator.join_uneven_inputs([ddp_model] ):
														for batch_idx, batch in enumerate(UpperCAmelCase__ ):
																					lowercase_					:			Any										=       ddp_model(batch[0].float() )
																					lowercase_					:			List[str]										=       output.sum()
																					loss.backward()
																					batch_idxs.append(UpperCAmelCase__ )

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

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




def 							lowerCamelCase	(      )			->      Any:
							lowercase_					:			str										=       True
							lowercase_					:			Tuple										=       False
							lowercase_					:			str										=       create_accelerator(even_batches=UpperCAmelCase__ )
							lowercase_					:			Union[str, Any]										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			Any										=       accelerator.prepare(UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )
							lowercase_					:			List[Any]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )

							with accelerator.join_uneven_inputs([ddp_model]      ,      even_batches=UpperCAmelCase__ ):
														lowercase_					:			Union[str, Any]										=       train_dl.batch_sampler.even_batches
														lowercase_					:			List[str]										=       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	(      )			->      Dict:
							lowercase_					:			str										=       True
							lowercase_					:			Optional[Any]										=       False
							lowercase_					:			Union[str, Any]										=       create_accelerator(even_batches=UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       torch.nn.Linear(1      ,      1 )
							lowercase_					:			Optional[int]										=       accelerator.prepare(UpperCAmelCase__ )
							create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1      ,      iterable=UpperCAmelCase__ )
							lowercase_					:			List[str]										=       create_dataloader(UpperCAmelCase__      ,      dataset_size=3      ,      batch_size=1 )

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

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

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




def 							lowerCamelCase	(      )			->      List[str]:
							lowercase_					:			List[Any]										=       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_					:			List[Any]										=       accelerator.state.distributed_type
							lowercase_					:			Union[str, Any]										=       DistributedType.FSDP
							test_join_raises_warning_for_non_ddp_distributed(UpperCAmelCase__ )
							lowercase_					:			str										=       original_state


if __name__ == "__main__":
						main()





