# 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   lowercase      (       a       ):




							def __init__(  self				:   Optional[int]		,							_UpperCamelCase				:   int )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    data





							def __iter__(  self				:   Tuple )   ->			Optional[int]:



												'''simple docstring'''

												for element in self.data:
																	yield element





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any]=True					):
					SCREAMING_SNAKE_CASE           =    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					):
					if iterable:
										SCREAMING_SNAKE_CASE           =    DummyIterableDataset(torch.as_tensor(range(UpperCAmelCase__					)					)					)
					else:
										SCREAMING_SNAKE_CASE           =    TensorDataset(torch.as_tensor(range(UpperCAmelCase__					)					)					)

					SCREAMING_SNAKE_CASE           =    DataLoader(UpperCAmelCase__ ,	batch_size=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    accelerator.prepare(UpperCAmelCase__					)

					return dl





def 		__lowerCamelCase							(UpperCAmelCase__       :							Accelerator ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							List[int] ,	UpperCAmelCase__       :							List[int] ,	):
					SCREAMING_SNAKE_CASE           =    create_dataloader(accelerator=UpperCAmelCase__ ,	dataset_size=UpperCAmelCase__ ,	batch_size=UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    [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							():
					SCREAMING_SNAKE_CASE           =    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							():
					SCREAMING_SNAKE_CASE           =    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							():
					SCREAMING_SNAKE_CASE           =    create_accelerator(even_batches=UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    torch.nn.Linear(1 ,	1					)
					SCREAMING_SNAKE_CASE           =    accelerator.prepare(UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    create_dataloader(UpperCAmelCase__ ,	dataset_size=3 ,	batch_size=1					)

					SCREAMING_SNAKE_CASE           =    []
					with accelerator.join_uneven_inputs([ddp_model]					):
										for batch_idx, batch in enumerate(UpperCAmelCase__					):
															SCREAMING_SNAKE_CASE           =    ddp_model(batch[0].float()					)
															SCREAMING_SNAKE_CASE           =    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__       :							int					):
					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							():
					SCREAMING_SNAKE_CASE           =    True
					SCREAMING_SNAKE_CASE           =    False
					SCREAMING_SNAKE_CASE           =    create_accelerator(even_batches=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    torch.nn.Linear(1 ,	1					)
					SCREAMING_SNAKE_CASE           =    accelerator.prepare(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    create_dataloader(UpperCAmelCase__ ,	dataset_size=3 ,	batch_size=1					)
					SCREAMING_SNAKE_CASE           =    create_dataloader(UpperCAmelCase__ ,	dataset_size=3 ,	batch_size=1					)

					with accelerator.join_uneven_inputs([ddp_model] ,	even_batches=UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    train_dl.batch_sampler.even_batches
										SCREAMING_SNAKE_CASE           =    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							():
					SCREAMING_SNAKE_CASE           =    True
					SCREAMING_SNAKE_CASE           =    False
					SCREAMING_SNAKE_CASE           =    create_accelerator(even_batches=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    torch.nn.Linear(1 ,	1					)
					SCREAMING_SNAKE_CASE           =    accelerator.prepare(UpperCAmelCase__					)
					create_dataloader(UpperCAmelCase__ ,	dataset_size=3 ,	batch_size=1 ,	iterable=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    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__					):
																				SCREAMING_SNAKE_CASE           =    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							():
					SCREAMING_SNAKE_CASE           =    create_accelerator()
					SCREAMING_SNAKE_CASE           =    torch.nn.Linear(1 ,	1					)
					SCREAMING_SNAKE_CASE           =    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							():
					SCREAMING_SNAKE_CASE           =    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"					)
					SCREAMING_SNAKE_CASE           =    accelerator.state.distributed_type
					SCREAMING_SNAKE_CASE           =    DistributedType.FSDP
					test_join_raises_warning_for_non_ddp_distributed(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    original_state


if __name__ == "__main__":
							main()





from random import randint, random





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							int = 5 ,	):
					SCREAMING_SNAKE_CASE           =    [[-1] * number_of_cells]  # Create a highway without any car
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    max(UpperCAmelCase__ ,	0					)
					while i < number_of_cells:
										SCREAMING_SNAKE_CASE           =    (
										    randint(0 ,	UpperCAmelCase__					) if random_speed else initial_speed
										)  # Place the cars
										i += (
										    randint(1 ,	max_speed * 2					) if random_frequency else frequency
										)  # Arbitrary number, may need tuning
					return highway





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    highway_now[car_index + 1 :]
					for cell in range(len(UpperCAmelCase__					)					):  # May need a better name for this
										if cells[cell] != -1:  # If the cell is not empty then
															return distance  # we have the distance we wanted
										distance += 1
					# Here if the car is near the end of the highway
					return distance + get_distance(UpperCAmelCase__ ,	-1					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					)
					# Beforce calculations, the highway is empty
					SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

					for car_index in range(UpperCAmelCase__					):
										if highway_now[car_index] != -1:
															# Add 1 to the current speed of the car and cap the speed
															SCREAMING_SNAKE_CASE           =    min(highway_now[car_index] + 1 ,	UpperCAmelCase__					)
															# Number of empty cell before the next car
															SCREAMING_SNAKE_CASE           =    get_distance(UpperCAmelCase__ ,	UpperCAmelCase__					) - 1
															# We can't have the car causing an accident
															SCREAMING_SNAKE_CASE           =    min(next_highway[car_index] ,	UpperCAmelCase__					)
															if random() < probability:
																				# Randomly, a driver will slow down
																				SCREAMING_SNAKE_CASE           =    max(next_highway[car_index] - 1 ,	0					)
					return next_highway





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(highway[0]					)

					for i in range(UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    update(highway[i] ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

										for car_index in range(UpperCAmelCase__					):
															SCREAMING_SNAKE_CASE           =    next_speeds_calculated[car_index]
															if speed != -1:
																				# Change the position based on the speed (with % to create the loop)
																				SCREAMING_SNAKE_CASE           =    (car_index + speed) % number_of_cells
																				# Commit the change of position
																				SCREAMING_SNAKE_CASE           =    speed
										highway.append(UpperCAmelCase__					)

					return highway


if __name__ == "__main__":
							import doctest

							doctest.testmod()




