






"""simple docstring"""





from random import randint, random



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    bool = False      ,   __UpperCAmelCase      :    bool = False      ,   __UpperCAmelCase      :    int = 5      ,   )    ->							list:
						__magic_name__:	Optional[int]							=       [[-1] * number_of_cells]  # Create a highway without any car
						__magic_name__:	Any							=       0
						__magic_name__:	str							=       max(__UpperCAmelCase      ,   0  )
						while i < number_of_cells:
												__magic_name__:	Optional[Any]							=       (
												    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   a    (     __UpperCAmelCase      :    list      ,   __UpperCAmelCase      :    int  )    ->							int:
						__magic_name__:	str							=       0
						__magic_name__:	Dict							=       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   a    (     __UpperCAmelCase      :    list      ,   __UpperCAmelCase      :    float      ,   __UpperCAmelCase      :    int  )    ->							list:
						__magic_name__:	List[Any]							=       len(__UpperCAmelCase  )
						# Beforce calculations, the highway is empty
						__magic_name__:	List[Any]							=       [-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
																		__magic_name__:	List[str]							=       min(highway_now[car_index] + 1      ,   __UpperCAmelCase  )
																		# Number of empty cell before the next car
																		__magic_name__:	List[str]							=       get_distance(__UpperCAmelCase      ,   __UpperCAmelCase  ) - 1
																		# We can't have the car causing an accident
																		__magic_name__:	Union[str, Any]							=       min(next_highway[car_index]      ,   __UpperCAmelCase  )
																		if random() < probability:
																								# Randomly, a driver will slow down
																								__magic_name__:	Optional[int]							=       max(next_highway[car_index] - 1      ,   0  )
						return next_highway



def   a    (     __UpperCAmelCase      :    list      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    float      ,   __UpperCAmelCase      :    int  )    ->							list:
						__magic_name__:	Dict							=       len(highway[0]  )

						for i in range(__UpperCAmelCase  ):
												__magic_name__:	Tuple							=       update(highway[i]      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
												__magic_name__:	List[str]							=       [-1] * number_of_cells

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

						return highway


if __name__ == "__main__":
							import doctest

							doctest.testmod()









# Copyright 2023 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 torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce





def 				A__ (snake_case   :		List[Any]				)     ->						Optional[Any]:
					return (torch.arange(state.num_processes				) + 1.0 + (state.num_processes * state.process_index)).to(state.device				)





def 				A__ (snake_case   :		int				)     ->						Any:
					__UpperCamelCase					:	List[str]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	List[Any]             =      gather(snake_case				)
					assert gathered_tensor.tolist() == list(range(1		,	state.num_processes**2 + 1				)				)





def 				A__ (snake_case   :		Optional[Any]				)     ->						Dict:
					__UpperCamelCase					:	Any             =      [state.process_index]
					__UpperCamelCase					:	int             =      gather_object(snake_case				)
					assert len(snake_case				) == state.num_processes, F'''{gathered_obj}, {len(snake_case				)} != {state.num_processes}'''
					assert gathered_obj == list(range(state.num_processes				)				), F'''{gathered_obj} != {list(range(state.num_processes				)				)}'''





def 				A__ (snake_case   :		Any				)     ->						Optional[Any]:
					__UpperCamelCase					:	Optional[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	Any             =      broadcast(snake_case				)
					assert broadcasted_tensor.shape == torch.Size([state.num_processes]				)
					assert broadcasted_tensor.tolist() == list(range(1		,	state.num_processes + 1				)				)





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					# We need to pad the tensor with one more element if we are the main process
					# to ensure that we can pad
					if state.is_main_process:
										__UpperCamelCase					:	List[Any]             =      torch.arange(state.num_processes + 1				).to(state.device				)
					else:
										__UpperCamelCase					:	List[str]             =      torch.arange(state.num_processes				).to(state.device				)
					__UpperCamelCase					:	List[str]             =      pad_across_processes(snake_case				)
					assert padded_tensor.shape == torch.Size([state.num_processes + 1]				)
					if not state.is_main_process:
										assert padded_tensor.tolist() == list(range(0		,	state.num_processes				)				) + [0]





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	Optional[int]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""sum"""				)
					__UpperCamelCase					:	Optional[int]             =      torch.tensor([4.0, 6]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Optional[Any]				)     ->						Optional[Any]:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	List[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""mean"""				)
					__UpperCamelCase					:	Union[str, Any]             =      torch.tensor([2.0, 3]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Tuple				)     ->						int:
					# For xla_spawn (TPUs)
					main()





def 				A__ ()     ->						List[Any]:
					__UpperCamelCase					:	List[Any]             =      PartialState()
					state.print(F'''State: {state}'''				)
					state.print("""testing gather"""				)
					test_gather(snake_case				)
					state.print("""testing gather_object"""				)
					test_gather_object(snake_case				)
					state.print("""testing broadcast"""				)
					test_broadcast(snake_case				)
					state.print("""testing pad_across_processes"""				)
					test_pad_across_processes(snake_case				)
					state.print("""testing reduce_sum"""				)
					test_reduce_sum(snake_case				)
					state.print("""testing reduce_mean"""				)
					test_reduce_mean(snake_case				)


if __name__ == "__main__":
	main()






