

# 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 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    def __init__(				self						,       a):
         lowercase__   :       List[str]				=	data





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



def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]=True      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	Accelerator(even_batches=SCREAMING_SNAKE_CASE_      )
     assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
     return accelerator

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Accelerator     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   bool = False      ):



     '''simple docstring'''





     if iterable:
          lowercase__   :       Optional[Any]				=	DummyIterableDataset(torch.as_tensor(range(SCREAMING_SNAKE_CASE_      )      )      )
     else:
          lowercase__   :       Any				=	TensorDataset(torch.as_tensor(range(SCREAMING_SNAKE_CASE_      )      )      )

     lowercase__   :       Optional[Any]				=	DataLoader(SCREAMING_SNAKE_CASE_     ,					batch_size=SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Optional[Any]				=	accelerator.prepare(SCREAMING_SNAKE_CASE_      )

     return dl

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Accelerator     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   List[int]     ,					SCREAMING_SNAKE_CASE_		:   List[int]     ,					):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	create_dataloader(accelerator=SCREAMING_SNAKE_CASE_     ,					dataset_size=SCREAMING_SNAKE_CASE_     ,					batch_size=SCREAMING_SNAKE_CASE_      )

     lowercase__   :       List[Any]				=	[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      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       str				=	create_accelerator()

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

def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       Union[str, Any]				=	create_accelerator(even_batches=SCREAMING_SNAKE_CASE_      )

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

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

def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	create_accelerator(even_batches=SCREAMING_SNAKE_CASE_      )

     lowercase__   :       Dict				=	torch.nn.Linear(1     ,					1      )
     lowercase__   :       Any				=	accelerator.prepare(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       str				=	create_dataloader(SCREAMING_SNAKE_CASE_     ,					dataset_size=3     ,					batch_size=1      )

     lowercase__   :       List[str]				=	[]
     with accelerator.join_uneven_inputs([ddp_model]      ):
          for batch_idx, batch in enumerate(SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       str				=	ddp_model(batch[0].float()      )
               lowercase__   :       int				=	output.sum()
               loss.backward()
               batch_idxs.append(SCREAMING_SNAKE_CASE_      )

     accelerator.wait_for_everyone()

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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





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

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

def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       int				=	True
     lowercase__   :       Optional[Any]				=	False
     lowercase__   :       Dict				=	create_accelerator(even_batches=SCREAMING_SNAKE_CASE_      )
     lowercase__   :       List[Any]				=	torch.nn.Linear(1     ,					1      )
     lowercase__   :       Any				=	accelerator.prepare(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Tuple				=	create_dataloader(SCREAMING_SNAKE_CASE_     ,					dataset_size=3     ,					batch_size=1      )
     lowercase__   :       Optional[int]				=	create_dataloader(SCREAMING_SNAKE_CASE_     ,					dataset_size=3     ,					batch_size=1      )

     with accelerator.join_uneven_inputs([ddp_model]     ,					even_batches=SCREAMING_SNAKE_CASE_      ):
          lowercase__   :       Tuple				=	train_dl.batch_sampler.even_batches
          lowercase__   :       Union[str, Any]				=	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      snake_case__      (   ):



     '''simple docstring'''





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

     with warnings.catch_warnings():
          warnings.filterwarnings('ignore'      )
          try:
               with accelerator.join_uneven_inputs([ddp_model]     ,					even_batches=SCREAMING_SNAKE_CASE_      ):
                    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      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       Tuple				=	create_accelerator()
     lowercase__   :       Optional[Any]				=	torch.nn.Linear(1     ,					1      )
     lowercase__   :       Optional[Any]				=	accelerator.prepare(SCREAMING_SNAKE_CASE_      )
     create_dataloader(SCREAMING_SNAKE_CASE_     ,					dataset_size=3     ,					batch_size=1     ,					iterable=SCREAMING_SNAKE_CASE_      )

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

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

def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       Optional[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__   :       Dict				=	accelerator.state.distributed_type
     lowercase__   :       Any				=	DistributedType.FSDP
     test_join_raises_warning_for_non_ddp_distributed(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       List[Any]				=	original_state


if __name__ == "__main__":
       main()




from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    def __init__(				self						,       a						,       a = None						,       a = None						,       a = True						,       a = None						,       a = False						,       a = None						,       a = True						,       a = "arrow"						,       **a						,       ):
         super().__init__(
             split=a						,       features=a						,       cache_dir=a						,       keep_in_memory=a						,       streaming=a						,       **a						,       )
         lowercase__   :       Optional[int]				=	load_from_cache_file
         lowercase__   :       Optional[int]				=	file_format
         lowercase__   :       int				=	Spark(
             df=a						,       features=a						,       cache_dir=a						,       working_dir=a						,       **a						,       )





    def 				snake_case_    (				self):
         if self.streaming:
              return self.builder.as_streaming_dataset(split=self.split)
         lowercase__   :       Dict				=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
         self.builder.download_and_prepare(
             download_mode=a						,       file_format=self._file_format						,       )
         return self.builder.as_dataset(split=self.split)

