'''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__  (		__lowerCamelCase    ):





   """simple docstring"""




   def __init__(	self					:  int		,  _lowercase					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= data



   def __iter__(	self					:  Dict							):



    """simple docstring"""



    for element in self.data:
     yield element




def       lowerCAmelCase_     (		lowercase:							List[str]=True		)    ->				str:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= Accelerator(even_batches=__a		)
 assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
 return accelerator




def       lowerCAmelCase_     (		lowercase:							Accelerator  ,					lowercase:							int  ,					lowercase:							int  ,					lowercase:							bool = False		)    ->				List[Any]:







 '''simple docstring'''




 if iterable:
  _UpperCamelCase:     Any							= DummyIterableDataset(torch.as_tensor(range(__a		)		)		)
 else:
  _UpperCamelCase:     List[Any]							= TensorDataset(torch.as_tensor(range(__a		)		)		)

 _UpperCamelCase:     int							= DataLoader(__a  ,					batch_size=__a		)
 _UpperCamelCase:     Any							= accelerator.prepare(__a		)

 return dl




def       lowerCAmelCase_     (		lowercase:							Accelerator  ,					lowercase:							int  ,					lowercase:							int  ,					lowercase:							List[int]  ,					lowercase:							List[int]  ,					)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= create_dataloader(accelerator=__a  ,					dataset_size=__a  ,					batch_size=__a		)

 _UpperCamelCase:     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_     (		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Union[str, Any]							= 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       lowerCAmelCase_     (		)    ->				Tuple:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= 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       lowerCAmelCase_     (		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Dict							= create_accelerator(even_batches=__a		)

 _UpperCamelCase:     List[Any]							= torch.nn.Linear(1  ,					1		)
 _UpperCamelCase:     Any							= accelerator.prepare(__a		)

 _UpperCamelCase:     Dict							= create_dataloader(__a  ,					dataset_size=3  ,					batch_size=1		)

 _UpperCamelCase:     int							= []
 with accelerator.join_uneven_inputs([ddp_model]		):
  for batch_idx, batch in enumerate(__a		):
   _UpperCamelCase:     Any							= ddp_model(batch[0].float()		)
   _UpperCamelCase:     Union[str, Any]							= 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       lowerCAmelCase_     (		lowercase:							List[Any]		)    ->				str:







 '''simple docstring'''




 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       lowerCAmelCase_     (		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Any							= True
 _UpperCamelCase:     Tuple							= False
 _UpperCamelCase:     List[str]							= create_accelerator(even_batches=__a		)
 _UpperCamelCase:     List[str]							= torch.nn.Linear(1  ,					1		)
 _UpperCamelCase:     Optional[Any]							= accelerator.prepare(__a		)
 _UpperCamelCase:     Optional[Any]							= create_dataloader(__a  ,					dataset_size=3  ,					batch_size=1		)
 _UpperCamelCase:     Tuple							= create_dataloader(__a  ,					dataset_size=3  ,					batch_size=1		)

 with accelerator.join_uneven_inputs([ddp_model]  ,					even_batches=__a		):
  _UpperCamelCase:     Dict							= train_dl.batch_sampler.even_batches
  _UpperCamelCase:     Dict							= 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_     (		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= True
 _UpperCamelCase:     str							= False
 _UpperCamelCase:     Any							= create_accelerator(even_batches=__a		)
 _UpperCamelCase:     int							= torch.nn.Linear(1  ,					1		)
 _UpperCamelCase:     List[str]							= accelerator.prepare(__a		)
 create_dataloader(__a  ,					dataset_size=3  ,					batch_size=1  ,					iterable=__a		)
 _UpperCamelCase:     str							= 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:     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_     (		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= create_accelerator()
 _UpperCamelCase:     Any							= torch.nn.Linear(1  ,					1		)
 _UpperCamelCase:     Optional[Any]							= 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       lowerCAmelCase_     (		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     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'''		)
 _UpperCamelCase:     Any							= accelerator.state.distributed_type
 _UpperCamelCase:     List[str]							= DistributedType.FSDP
 test_join_raises_warning_for_non_ddp_distributed(__a		)
 _UpperCamelCase:     Any							= original_state


if __name__ == "__main__":
  main()
from __future__ import annotations




def       lowerCAmelCase_     (		lowercase:							int = 4		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     Dict							= abs(lowercase		) or 4
 return [[1 + x + y * row_size for x in range(lowercase		)] for y in range(lowercase		)]




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_row(transpose(lowercase		)		)
 # OR.. transpose(reverse_column(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_row(reverse_column(lowercase		)		)
 # OR.. reverse_column(reverse_row(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_column(transpose(lowercase		)		)
 # OR.. transpose(reverse_row(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     int							= [list(lowercase		) for x in zip(*lowercase		)]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     str							= matrix[::-1]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     str							= [x[::-1] for x in matrix]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				None:







 '''simple docstring'''
 for i in matrix:
  print(*lowercase		)


if __name__ == "__main__":
  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 90 counterclockwise:\n''')
  print_matrix(rotate_aa(matrix))

  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 180:\n''')
  print_matrix(rotate_aaa(matrix))

  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 270 counterclockwise:\n''')
  print_matrix(rotate_aaa(matrix))