



'''simple docstring'''




from __future__ import annotations

from typing import TypedDict

class 			UpperCamelCase_ (							__magic_name__			):
	lowercase          = 42
	lowercase          = 42




def       __lowerCamelCase  (					_lowercase		)      ->			list[str]:
							if not isinstance(_lowercase	,				_lowercase		):
														raise TypeError("""The parameter s type must be str."""		)

							return [s[i:] + s[:i] for i in range(len(_lowercase		)		)]



def       __lowerCamelCase  (					_lowercase		)      ->			BWTTransformDict:
							if not isinstance(_lowercase	,				_lowercase		):
														raise TypeError("""The parameter s type must be str."""		)
							if not s:
														raise ValueError("""The parameter s must not be empty."""		)

							UpperCAmelCase     :		str							     =		all_rotations(_lowercase		)
							rotations.sort()  # sort the list of rotations in alphabetically order
							# make a string composed of the last char of each rotation
							UpperCAmelCase     :		BWTTransformDict							     =		{
							    "bwt_string": "".join([word[-1] for word in rotations]		),
							    "idx_original_string": rotations.index(_lowercase		),
							}
							return response



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			str:
							if not isinstance(_lowercase	,				_lowercase		):
														raise TypeError("""The parameter bwt_string type must be str."""		)
							if not bwt_string:
														raise ValueError("""The parameter bwt_string must not be empty."""		)
							try:
														UpperCAmelCase     :		Any							     =		int(_lowercase		)
							except ValueError:
														raise TypeError(
														    """The parameter idx_original_string type must be int or passive"""
														    """ of cast to int."""		)
							if idx_original_string < 0:
														raise ValueError("""The parameter idx_original_string must not be lower than 0."""		)
							if idx_original_string >= len(_lowercase		):
														raise ValueError(
														    """The parameter idx_original_string must be lower than""" """ len(bwt_string)."""		)

							UpperCAmelCase     :		List[Any]							     =		[""""""] * len(_lowercase		)
							for _ in range(len(_lowercase		)		):
														for i in range(len(_lowercase		)		):
																					UpperCAmelCase     :		Union[str, Any]							     =		bwt_string[i] + ordered_rotations[i]
														ordered_rotations.sort()
							return ordered_rotations[idx_original_string]


if __name__ == "__main__":
							a   :			Tuple					  =    """Provide a string that I will generate its BWT transform: """
							a   :			int					  =    input(entry_msg).strip()
							a   :			Optional[int]					  =    bwt_transform(s)
							print(
							    F'''Burrows Wheeler transform for string \'{s}\' results '''
							    F'''in \'{result['bwt_string']}\''''
							)
							a   :			List[str]					  =    reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
							print(
							    F'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
							    F'''we get original string \'{original_string}\''''
							)






'''simple docstring'''




# 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       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							return (torch.arange(state.num_processes		) + 1.0 + (state.num_processes * state.process_index)).to(state.device		)



def       __lowerCamelCase  (					_lowercase		)      ->			List[Any]:
							UpperCAmelCase     :		Any							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Union[str, Any]							     =		gather(_lowercase		)
							assert gathered_tensor.tolist() == list(range(1	,				state.num_processes**2 + 1		)		)



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Any							     =		[state.process_index]
							UpperCAmelCase     :		Union[str, Any]							     =		gather_object(_lowercase		)
							assert len(_lowercase		) == state.num_processes, F'''{gathered_obj}, {len(_lowercase		)} != {state.num_processes}'''
							assert gathered_obj == list(range(state.num_processes		)		), F'''{gathered_obj} != {list(range(state.num_processes		)		)}'''



def       __lowerCamelCase  (					_lowercase		)      ->			List[Any]:
							UpperCAmelCase     :		Optional[int]							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		List[str]							     =		broadcast(_lowercase		)
							assert broadcasted_tensor.shape == torch.Size([state.num_processes]		)
							assert broadcasted_tensor.tolist() == list(range(1	,				state.num_processes + 1		)		)



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							# 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     :		Optional[Any]							     =		torch.arange(state.num_processes + 1		).to(state.device		)
							else:
														UpperCAmelCase     :		Tuple							     =		torch.arange(state.num_processes		).to(state.device		)
							UpperCAmelCase     :		Optional[Any]							     =		pad_across_processes(_lowercase		)
							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       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							# For now runs on only two processes
							if state.num_processes != 2:
														return
							UpperCAmelCase     :		Optional[Any]							     =		create_tensor(_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		reduce(_lowercase	,				"""sum"""		)
							UpperCAmelCase     :		Optional[Any]							     =		torch.tensor([4.0, 6]		).to(state.device		)
							assert torch.allclose(_lowercase	,				_lowercase		), F'''{reduced_tensor} != {truth_tensor}'''



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



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							# For xla_spawn (TPUs)
							main()



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


if __name__ == "__main__":
							main()

