




'''simple docstring'''





#
# Sort large text files in a minimum amount of memory
#
import argparse
import os




class 				_snake_case	:
			lowerCAmelCase_  :   List[Any]		 =					"block_{0}.dat"




			def __init__(      self       ,							a__							)       ->       List[str]:

										'''simple docstring'''




										snake_case_			      =   filename
										snake_case_			      =   []




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   self.BLOCK_FILENAME_FORMAT.format(a__							)
										with open(a__       ,							"w"							) as file:
																	file.write(a__							)
										self.block_filenames.append(a__							)




			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										return self.block_filenames




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__=None							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   0
										with open(self.filename							) as file:
																	while True:
																								snake_case_			      =   file.readlines(a__							)

																								if lines == []:
																															break

																								if sort_key is None:
																															lines.sort()
																								else:
																															lines.sort(key=a__							)

																								self.write_block("".join(a__							)       ,							a__							)
																								i += 1




			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										map(os.remove       ,							self.block_filenames							)




class 				_snake_case	:




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   -1
										snake_case_			      =   None

										for i in range(len(a__							)							):
																	if min_str is None or choices[i] < min_str:
																								snake_case_			      =   i

										return min_index




class 				_snake_case	:




			def __init__(      self       ,							a__							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   files
										snake_case_			      =   set()
										snake_case_			      =   len(a__							)
										snake_case_			      =   {i: None for i in range(self.num_buffers							)}




			def      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										return {
										    i: self.buffers[i] for i in range(self.num_buffers							) if i not in self.empty
										}




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										for i in range(self.num_buffers							):
																	if self.buffers[i] is None and i not in self.empty:
																								snake_case_			      =   self.files[i].readline()

																								if self.buffers[i] == "":
																															self.empty.add(a__							)
																															self.files[i].close()

										if len(self.empty							) == self.num_buffers:
																	return False

										return True




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   self.buffers[index]
										snake_case_			      =   None

										return value




class 				_snake_case	:




			def __init__(      self       ,							a__							)       ->       Any:

										'''simple docstring'''




										snake_case_			      =   merge_strategy




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__       ,							a__							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   FilesArray(self.get_file_handles(a__       ,							a__							)							)
										with open(a__       ,							"w"       ,							a__							) as outfile:
																	while buffers.refresh():
																								snake_case_			      =   self.merge_strategy.select(buffers.get_dict()							)
																								outfile.write(buffers.unshift(a__							)							)




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   {}

										for i in range(len(a__							)							):
																	snake_case_			      =   open(filenames[i]       ,							"r"       ,							a__							)  # noqa: UP015

										return files




class 				_snake_case	:




			def __init__(      self       ,							a__							)       ->       List[Any]:

										'''simple docstring'''




										snake_case_			      =   block_size




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__=None							)       ->       str:

										'''simple docstring'''




										snake_case_			      =   self.get_number_blocks(a__       ,							self.block_size							)
										snake_case_			      =   FileSplitter(a__							)
										splitter.split(self.block_size       ,							a__							)

										snake_case_			      =   FileMerger(NWayMerge()							)
										snake_case_			      =   self.block_size / (num_blocks + 1)
										merger.merge(splitter.get_block_filenames()       ,							filename + ".out"       ,							a__							)

										splitter.cleanup()




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__							)       ->       List[Any]:

										'''simple docstring'''




										return (os.stat(a__							).st_size / block_size) + 1


def   UpperCamelCase_(			snake_case							: List[Any]  ):

							'''simple docstring'''






							if string[-1].lower() == "k":
														return int(string[:-1]  ) * 1_0_2_4
							elif string[-1].lower() == "m":
														return int(string[:-1]  ) * 1_0_2_4 * 1_0_2_4
							elif string[-1].lower() == "g":
														return int(string[:-1]  ) * 1_0_2_4 * 1_0_2_4 * 1_0_2_4
							else:
														return int(snake_case  )


def   UpperCamelCase_(			):

							'''simple docstring'''






							snake_case_			      =   argparse.ArgumentParser()
							parser.add_argument(
							    "-m"							,       "--mem"							,       help="amount of memory to use for sorting"							,       default="100M"  )
							parser.add_argument(
							    "filename"							,       metavar="<filename>"							,       nargs=1							,       help="name of file to sort"  )
							snake_case_			      =   parser.parse_args()

							snake_case_			      =   ExternalSort(parse_memory(args.mem  )  )
							sorter.sort(args.filename[0]  )


if __name__ == "__main__":
		main()










'''simple docstring'''





import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm


def   UpperCamelCase_(			snake_case							: int							,       snake_case							: bool = False  ):

							'''simple docstring'''






							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(snake_case  )
							np.random.seed(snake_case  )
							torch.manual_seed(snake_case  )
							if is_xpu_available():
														torch.xpu.manual_seed_all(snake_case  )
							elif is_npu_available():
														torch.npu.manual_seed_all(snake_case  )
							else:
														torch.cuda.manual_seed_all(snake_case  )
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(snake_case  )


def   UpperCamelCase_(			snake_case							: Optional[RNGType] = None							,       snake_case							: Optional[torch.Generator] = None  ):

							'''simple docstring'''






							if rng_type == RNGType.TORCH:
														snake_case_			      =   torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														snake_case_			      =   torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														snake_case_			      =   torch.tensor(xm.get_rng_state()  )
							elif rng_type == RNGType.NPU:
														assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
														snake_case_			      =   torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														snake_case_			      =   torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														snake_case_			      =   generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							snake_case_			      =   AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														snake_case_			      =   rng_state.to(xm.xla_device()  )
														xm.collective_broadcast([rng_state]  )
														xm.mark_step()
														snake_case_			      =   rng_state.cpu()
							elif (
							    state.distributed_type in CUDA_DISTRIBUTED_TYPES
							    or state.distributed_type == DistributedType.MULTI_NPU
							    or state.distributed_type == DistributedType.MULTI_XPU
							):
														snake_case_			      =   rng_state.to(state.device  )
														torch.distributed.broadcast(snake_case							,       0  )
														snake_case_			      =   rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(snake_case							,       0  )

							# Set the broadcast rng state
							if rng_type == RNGType.TORCH:
														torch.set_rng_state(snake_case  )
							elif rng_type == RNGType.CUDA:
														torch.cuda.set_rng_state(snake_case  )
							elif rng_type == RNGType.NPU:
														torch.npu.set_rng_state(snake_case  )
							elif rng_type == RNGType.XPU:
														torch.xpu.set_rng_state(snake_case  )
							elif rng_type == RNGType.XLA:
														xm.set_rng_state(rng_state.item()  )
							elif rng_type == RNGType.GENERATOR:
														generator.set_state(snake_case  )




def   UpperCamelCase_(			snake_case							: List[Union[str, RNGType]]							,       snake_case							: Optional[torch.Generator] = None  ):

							'''simple docstring'''






							for rng_type in rng_types:
														synchronize_rng_state(RNGType(snake_case  )							,       generator=snake_case  )




