


# 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  (				_snake_case							):




 def __init__(		self ,  _lowerCamelCase   ):
       a						:Optional[int]										=	data




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







def     __lowerCamelCase						(			UpperCAmelCase_						:		Union[str, Any]=True						):




      """simple docstring"""





      a						:List[str]										=	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						):




      """simple docstring"""





      if iterable:
            a						:Tuple										=	DummyIterableDataset(torch.as_tensor(range(UpperCAmelCase_						)						)						)
      else:
            a						:int										=	TensorDataset(torch.as_tensor(range(UpperCAmelCase_						)						)						)

      a						:Optional[Any]										=	DataLoader(UpperCAmelCase_			,  batch_size=UpperCAmelCase_						)
      a						:str										=	accelerator.prepare(UpperCAmelCase_						)

      return dl
def     __lowerCamelCase						(			UpperCAmelCase_						:		Accelerator			,  UpperCAmelCase_						:		int			,  UpperCAmelCase_						:		int			,  UpperCAmelCase_						:		List[int]			,  UpperCAmelCase_						:		List[int]			,  ):




      """simple docstring"""





      a						:Any										=	create_dataloader(accelerator=UpperCAmelCase_			,  dataset_size=UpperCAmelCase_			,  batch_size=UpperCAmelCase_						)

      a						:Optional[int]										=	[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						(			):




      """simple docstring"""





      a						:Dict										=	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						(			):




      """simple docstring"""





      a						: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						(			):




      """simple docstring"""





      a						:Optional[int]										=	create_accelerator(even_batches=UpperCAmelCase_						)

      a						:Any										=	torch.nn.Linear(1			,  1						)
      a						:int										=	accelerator.prepare(UpperCAmelCase_						)

      a						:int										=	create_dataloader(UpperCAmelCase_			,  dataset_size=3			,  batch_size=1						)

      a						:Optional[Any]										=	[]
      with accelerator.join_uneven_inputs([ddp_model]						):
            for batch_idx, batch in enumerate(UpperCAmelCase_						):
                  a						:Optional[int]										=	ddp_model(batch[0].float()						)
                  a						:List[Any]										=	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_						:		Dict						):




      """simple docstring"""





      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						(			):




      """simple docstring"""





      a						:List[str]										=	True
      a						:int										=	False
      a						:Union[str, Any]										=	create_accelerator(even_batches=UpperCAmelCase_						)
      a						:int										=	torch.nn.Linear(1			,  1						)
      a						:Union[str, Any]										=	accelerator.prepare(UpperCAmelCase_						)
      a						:int										=	create_dataloader(UpperCAmelCase_			,  dataset_size=3			,  batch_size=1						)
      a						:Optional[int]										=	create_dataloader(UpperCAmelCase_			,  dataset_size=3			,  batch_size=1						)

      with accelerator.join_uneven_inputs([ddp_model]			,  even_batches=UpperCAmelCase_						):
            a						:List[str]										=	train_dl.batch_sampler.even_batches
            a						:Optional[int]										=	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						(			):




      """simple docstring"""





      a						:List[Any]										=	True
      a						:Dict										=	False
      a						:Optional[int]										=	create_accelerator(even_batches=UpperCAmelCase_						)
      a						:Any										=	torch.nn.Linear(1			,  1						)
      a						:str										=	accelerator.prepare(UpperCAmelCase_						)
      create_dataloader(UpperCAmelCase_			,  dataset_size=3			,  batch_size=1			,  iterable=UpperCAmelCase_						)
      a						:Optional[int]										=	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_						):
                        a						:List[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						(			):




      """simple docstring"""





      a						:List[str]										=	create_accelerator()
      a						:List[str]										=	torch.nn.Linear(1			,  1						)
      a						:Optional[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						(			):




      """simple docstring"""





      a						:List[str]										=	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						:str										=	accelerator.state.distributed_type
      a						:Tuple										=	DistributedType.FSDP
      test_join_raises_warning_for_non_ddp_distributed(UpperCAmelCase_						)
      a						:Optional[int]										=	original_state


if __name__ == "__main__":
     main()









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


snake_case    :   Optional[Any]	    =					logging.get_logger(__name__)

snake_case    :   Dict	    =					{
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'data2vec-vision'




 def __init__(		self ,  _lowerCamelCase=768 ,  _lowerCamelCase=12 ,  _lowerCamelCase=12 ,  _lowerCamelCase=3072 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.0 ,  _lowerCamelCase=0.0 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-12 ,  _lowerCamelCase=224 ,  _lowerCamelCase=16 ,  _lowerCamelCase=3 ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=True ,  _lowerCamelCase=[3, 5, 7, 11] ,  _lowerCamelCase=[1, 2, 3, 6] ,  _lowerCamelCase=True ,  _lowerCamelCase=0.4 ,  _lowerCamelCase=256 ,  _lowerCamelCase=1 ,  _lowerCamelCase=False ,  _lowerCamelCase=255 ,  **_lowerCamelCase ,  ):
       super().__init__(**_lowerCamelCase   )

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

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







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	version.parse('1.11'							)




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




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return 1e-4





