




'''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     _a						(			__lowerCamelCase				):
       def __init__(			self		:			int     ,       lowercase		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	data







       def __iter__(			self		:			Optional[int]       ):



           '''simple docstring'''

           for element in self.data:
               yield element






def 		snake_case_							(_a			:							List[str]=True  ):
    UpperCAmelCase     =	Accelerator(even_batches=__a  )
    assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
    return accelerator





def 		snake_case_							(_a			:							Accelerator					,     _a			:							int					,     _a			:							int					,     _a			:							bool = False  ):




    if iterable:
        UpperCAmelCase     =	DummyIterableDataset(torch.as_tensor(range(__a  )  )  )
    else:
        UpperCAmelCase     =	TensorDataset(torch.as_tensor(range(__a  )  )  )

    UpperCAmelCase     =	DataLoader(__a					,     batch_size=__a  )
    UpperCAmelCase     =	accelerator.prepare(__a  )

    return dl





def 		snake_case_							(_a			:							Accelerator					,     _a			:							int					,     _a			:							int					,     _a			:							List[int]					,     _a			:							List[int]					,     ):
    UpperCAmelCase     =	create_dataloader(accelerator=__a					,     dataset_size=__a					,     batch_size=__a  )

    UpperCAmelCase     =	[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_							():
    UpperCAmelCase     =	create_accelerator()

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





def 		snake_case_							():
    UpperCAmelCase     =	create_accelerator(even_batches=__a  )

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

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





def 		snake_case_							():
    UpperCAmelCase     =	create_accelerator(even_batches=__a  )

    UpperCAmelCase     =	torch.nn.Linear(1					,     1  )
    UpperCAmelCase     =	accelerator.prepare(__a  )

    UpperCAmelCase     =	create_dataloader(__a					,     dataset_size=3					,     batch_size=1  )

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

    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_							(_a			:							List[Any]  ):




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

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





def 		snake_case_							():
    UpperCAmelCase     =	True
    UpperCAmelCase     =	False
    UpperCAmelCase     =	create_accelerator(even_batches=__a  )
    UpperCAmelCase     =	torch.nn.Linear(1					,     1  )
    UpperCAmelCase     =	accelerator.prepare(__a  )
    UpperCAmelCase     =	create_dataloader(__a					,     dataset_size=3					,     batch_size=1  )
    UpperCAmelCase     =	create_dataloader(__a					,     dataset_size=3					,     batch_size=1  )

    with accelerator.join_uneven_inputs([ddp_model]					,     even_batches=__a  ):
        UpperCAmelCase     =	train_dl.batch_sampler.even_batches
        UpperCAmelCase     =	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_							():
    UpperCAmelCase     =	True
    UpperCAmelCase     =	False
    UpperCAmelCase     =	create_accelerator(even_batches=__a  )
    UpperCAmelCase     =	torch.nn.Linear(1					,     1  )
    UpperCAmelCase     =	accelerator.prepare(__a  )
    create_dataloader(__a					,     dataset_size=3					,     batch_size=1					,     iterable=__a  )
    UpperCAmelCase     =	create_dataloader(__a					,     dataset_size=3					,     batch_size=1  )

    with warnings.catch_warnings():
        warnings.filterwarnings('''ignore'''  )
        try:
            with accelerator.join_uneven_inputs([ddp_model]					,     even_batches=__a  ):
                UpperCAmelCase     =	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_							():
    UpperCAmelCase     =	create_accelerator()
    UpperCAmelCase     =	torch.nn.Linear(1					,     1  )
    UpperCAmelCase     =	accelerator.prepare(__a  )
    create_dataloader(__a					,     dataset_size=3					,     batch_size=1					,     iterable=__a  )

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

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





def 		snake_case_							():
    UpperCAmelCase     =	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'''  )
    UpperCAmelCase     =	accelerator.state.distributed_type
    UpperCAmelCase     =	DistributedType.FSDP
    test_join_raises_warning_for_non_ddp_distributed(__a  )
    UpperCAmelCase     =	original_state


if __name__ == "__main__":
   main()









'''simple docstring'''







from __future__ import annotations

import math


class     _a						:
       def __init__(			self		:			Dict     ,       lowercase		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	size
           # approximate the overall size of segment tree with given value
           UpperCAmelCase     =	[0 for i in range(0     ,       4 * size       )]
           # create array to store lazy update
           UpperCAmelCase     =	[0 for i in range(0     ,       4 * size       )]
           UpperCAmelCase     =	[0 for i in range(0     ,       4 * size       )]  # flag for lazy update
       def 					A   (			self		:			List[str]     ,       lowercase		:			int       ):



           '''simple docstring'''

           return idx * 2
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			int       ):



           '''simple docstring'''

           return idx * 2 + 1
       def 					A   (			self		:			Dict     ,       lowercase		:			int     ,       lowercase		:			int     ,       lowercase		:			int     ,       lowercase		:			list[int]       ):



           '''simple docstring'''

           if left_element == right_element:
               UpperCAmelCase     =	a[left_element - 1]
           else:
               UpperCAmelCase     =	(left_element + right_element) // 2
               self.build(self.left(lowercase       )     ,       lowercase     ,       lowercase     ,       lowercase       )
               self.build(self.right(lowercase       )     ,       mid + 1     ,       lowercase     ,       lowercase       )
               UpperCAmelCase     =	max(
                   self.segment_tree[self.left(lowercase       )]     ,       self.segment_tree[self.right(lowercase       )]       )
       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			int     ,       lowercase		:			int     ,       lowercase		:			int     ,       lowercase		:			int     ,       lowercase		:			int     ,       lowercase		:			int       ):



           '''simple docstring'''

           if self.flag[idx] is True:
               UpperCAmelCase     =	self.lazy[idx]
               UpperCAmelCase     =	False
               if left_element != right_element:
                   UpperCAmelCase     =	self.lazy[idx]
                   UpperCAmelCase     =	self.lazy[idx]
                   UpperCAmelCase     =	True
                   UpperCAmelCase     =	True

           if right_element < a or left_element > b:
               return True
           if left_element >= a and right_element <= b:
               UpperCAmelCase     =	val
               if left_element != right_element:
                   UpperCAmelCase     =	val
                   UpperCAmelCase     =	val
                   UpperCAmelCase     =	True
                   UpperCAmelCase     =	True
               return True
           UpperCAmelCase     =	(left_element + right_element) // 2
           self.update(self.left(lowercase       )     ,       lowercase     ,       lowercase     ,       lowercase     ,       lowercase     ,       lowercase       )
           self.update(self.right(lowercase       )     ,       mid + 1     ,       lowercase     ,       lowercase     ,       lowercase     ,       lowercase       )
           UpperCAmelCase     =	max(
               self.segment_tree[self.left(lowercase       )]     ,       self.segment_tree[self.right(lowercase       )]       )
           return True
       def 					A   (			self		:			Any     ,       lowercase		:			int     ,       lowercase		:			int     ,       lowercase		:			int     ,       lowercase		:			int     ,       lowercase		:			int       ):



           '''simple docstring'''

           if self.flag[idx] is True:
               UpperCAmelCase     =	self.lazy[idx]
               UpperCAmelCase     =	False
               if left_element != right_element:
                   UpperCAmelCase     =	self.lazy[idx]
                   UpperCAmelCase     =	self.lazy[idx]
                   UpperCAmelCase     =	True
                   UpperCAmelCase     =	True
           if right_element < a or left_element > b:
               return -math.inf
           if left_element >= a and right_element <= b:
               return self.segment_tree[idx]
           UpperCAmelCase     =	(left_element + right_element) // 2
           UpperCAmelCase     =	self.query(self.left(lowercase       )     ,       lowercase     ,       lowercase     ,       lowercase     ,       lowercase       )
           UpperCAmelCase     =	self.query(self.right(lowercase       )     ,       mid + 1     ,       lowercase     ,       lowercase     ,       lowercase       )
           return max(lowercase     ,       lowercase       )







       def __str__(			self		:			Any       ):



           '''simple docstring'''

           return str([self.query(1     ,       1     ,       self.size     ,       lowercase     ,       lowercase       ) for i in range(1     ,       self.size + 1       )]       )


if __name__ == "__main__":
   A									=[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
   A									=15
   A									=SegmentTree(size)
   segt.build(1, 1, size, A)
   print(segt.query(1, 1, size, 4, 6))
   print(segt.query(1, 1, size, 7, 11))
   print(segt.query(1, 1, size, 7, 12))
   segt.update(1, 1, size, 1, 3, 1_11)
   print(segt.query(1, 1, size, 1, 15))
   segt.update(1, 1, size, 7, 8, 2_35)
   print(segt)




