






# 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   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				Optional[int]						=						data



		def __iter__(							self       )     ->							Optional[int]:
							for element in self.data:
												yield element





def 					lowercase__				(					__snake_case   :							Optional[Any]=True  ):


					'''simple docstring'''


					UpperCAmelCase_   :				str						=						Accelerator(even_batches=__snake_case  )
					assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
					return accelerator




def 					lowercase__				(					__snake_case   :							Accelerator					,	__snake_case   :							int					,	__snake_case   :							int					,	__snake_case   :							bool = False  ):


					'''simple docstring'''


					if iterable:
										UpperCAmelCase_   :				Optional[int]						=						DummyIterableDataset(torch.as_tensor(range(__snake_case  )  )  )
					else:
										UpperCAmelCase_   :				List[Any]						=						TensorDataset(torch.as_tensor(range(__snake_case  )  )  )

					UpperCAmelCase_   :				Dict						=						DataLoader(__snake_case					,	batch_size=__snake_case  )
					UpperCAmelCase_   :				Tuple						=						accelerator.prepare(__snake_case  )

					return dl




def 					lowercase__				(					__snake_case   :							Accelerator					,	__snake_case   :							int					,	__snake_case   :							int					,	__snake_case   :							List[int]					,	__snake_case   :							List[int]					,	):


					'''simple docstring'''


					UpperCAmelCase_   :				List[Any]						=						create_dataloader(accelerator=__snake_case					,	dataset_size=__snake_case					,	batch_size=__snake_case  )

					UpperCAmelCase_   :				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 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						create_accelerator()

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




def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				str						=						create_accelerator(even_batches=__snake_case  )

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




def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						create_accelerator(even_batches=__snake_case  )

					UpperCAmelCase_   :				List[Any]						=						torch.nn.Linear(1					,	1  )
					UpperCAmelCase_   :				Optional[Any]						=						accelerator.prepare(__snake_case  )

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

					UpperCAmelCase_   :				int						=						[]
					with accelerator.join_uneven_inputs([ddp_model]  ):
										for batch_idx, batch in enumerate(__snake_case  ):
															UpperCAmelCase_   :				Union[str, Any]						=						ddp_model(batch[0].float()  )
															UpperCAmelCase_   :				Optional[int]						=						output.sum()
															loss.backward()
															batch_idxs.append(__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 					lowercase__				(					__snake_case   :							str  ):


					'''simple docstring'''


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

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




def 					lowercase__				(					):


					'''simple docstring'''


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

					with accelerator.join_uneven_inputs([ddp_model]					,	even_batches=__snake_case  ):
										UpperCAmelCase_   :				List[str]						=						train_dl.batch_sampler.even_batches
										UpperCAmelCase_   :				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 					lowercase__				(					):


					'''simple docstring'''


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


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[int]						=						create_accelerator()
					UpperCAmelCase_   :				str						=						torch.nn.Linear(1					,	1  )
					UpperCAmelCase_   :				Any						=						accelerator.prepare(__snake_case  )
					create_dataloader(__snake_case					,	dataset_size=3					,	batch_size=1					,	iterable=__snake_case  )

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

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




def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						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_   :				Optional[Any]						=						DistributedType.FSDP
					test_join_raises_warning_for_non_ddp_distributed(__snake_case  )
					UpperCAmelCase_   :				List[str]						=						original_state


if __name__ == "__main__":
							main()














import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

__UpperCAmelCase     							=						'Usage of script: script_name <size_of_canvas:int>'

__UpperCAmelCase     							=						[0] * 100 + [1] * 10
random.shuffle(choice)




def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						[[False for i in range(__snake_case  )] for j in range(__snake_case  )]
					return canvas




def 					lowercase__				(					__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					for i, row in enumerate(__snake_case  ):
										for j, _ in enumerate(__snake_case  ):
															UpperCAmelCase_   :				Tuple						=						bool(random.getrandbits(1  )  )




def 					lowercase__				(					__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				List[Any]						=						np.array(__snake_case  )
					UpperCAmelCase_   :				Any						=						np.array(create_canvas(current_canvas.shape[0]  )  )
					for r, row in enumerate(__snake_case  ):
										for c, pt in enumerate(__snake_case  ):
															UpperCAmelCase_   :				Optional[int]						=						__judge_point(
															    __snake_case					,	current_canvas[r - 1 : r + 2, c - 1 : c + 2]  )

					UpperCAmelCase_   :				List[Any]						=						next_gen_canvas
					del next_gen_canvas  # cleaning memory as we move on.
					UpperCAmelCase_   :				list[list[bool]]						=						current_canvas.tolist()
					return return_canvas




def 					lowercase__				(					__snake_case   :							bool					,	__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[int]						=						0
					UpperCAmelCase_   :				List[Any]						=						0
					# finding dead or alive neighbours count.
					for i in neighbours:
										for status in i:
															if status:
																				alive += 1
															else:
																				dead += 1

    # handling duplicate entry for focus pt.
					if pt:
										alive -= 1
					else:
										dead -= 1

					# running the rules of game here.
					UpperCAmelCase_   :				List[Any]						=						pt
					if pt:
										if alive < 2:
															UpperCAmelCase_   :				str						=						False
										elif alive == 2 or alive == 3:
															UpperCAmelCase_   :				int						=						True
										elif alive > 3:
															UpperCAmelCase_   :				List[Any]						=						False
					else:
										if alive == 3:
															UpperCAmelCase_   :				int						=						True

					return state


if __name__ == "__main__":
		if len(sys.argv) != 2:
				raise Exception(usage_doc)

		__UpperCAmelCase     							=						int(sys.argv[1])
		# main working structure of this module.
		__UpperCAmelCase     							=						create_canvas(canvas_size)
		seed(c)
		__UpperCAmelCase							,       __UpperCAmelCase     							=						plt.subplots()
		fig.show()
		__UpperCAmelCase     							=						ListedColormap(['w', 'k'])
		try:
				while True:
						__UpperCAmelCase     							=						run(c)
						ax.matshow(c, cmap=cmap)
						fig.canvas.draw()
						ax.cla()
		except KeyboardInterrupt:
				# do nothing.
				pass






