


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






class 					lowerCAmelCase_     :
       __UpperCAmelCase				='block_{0}.dat'






       def __init__(   self ,    _UpperCamelCase      )->			List[str]:
            _A				=       filename
            _A				=       []






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase      )->			Optional[Any]:
            _A				=       self.BLOCK_FILENAME_FORMAT.format(_UpperCamelCase      )
            with open(_UpperCamelCase ,    'w'      ) as file:
                 file.write(_UpperCamelCase      )
            self.block_filenames.append(_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			Union[str, Any]:
            return self.block_filenames






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase=None      )->			List[str]:
            _A				=       0
            with open(self.filename      ) as file:
                 while True:
                      _A				=       file.readlines(_UpperCamelCase      )

                      if lines == []:
                           break

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

                      self.write_block(''.join(_UpperCamelCase      ) ,    _UpperCamelCase      )
                      i += 1






       def      UpperCamelCase	(   self      )->			List[str]:
            map(os.remove ,    self.block_filenames      )






class 					lowerCAmelCase_     :






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			List[Any]:
            _A				=       -1
            _A				=       None

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

            return min_index






class 					lowerCAmelCase_     :






       def __init__(   self ,    _UpperCamelCase      )->			List[str]:
            _A				=       files
            _A				=       set()
            _A				=       len(_UpperCamelCase      )
            _A				=       {i: None for i in range(self.num_buffers      )}






       def      UpperCamelCase	(   self      )->			Dict:
            return {
                i: self.buffers[i] for i in range(self.num_buffers      ) if i not in self.empty
            }






       def      UpperCamelCase	(   self      )->			int:
            for i in range(self.num_buffers      ):
                 if self.buffers[i] is None and i not in self.empty:
                      _A				=       self.files[i].readline()

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

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

            return True






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			Optional[int]:
            _A				=       self.buffers[index]
            _A				=       None

            return value






class 					lowerCAmelCase_     :






       def __init__(   self ,    _UpperCamelCase      )->			List[Any]:
            _A				=       merge_strategy






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			int:
            _A				=       FilesArray(self.get_file_handles(_UpperCamelCase ,    _UpperCamelCase      )      )
            with open(_UpperCamelCase ,    'w' ,    _UpperCamelCase      ) as outfile:
                 while buffers.refresh():
                      _A				=       self.merge_strategy.select(buffers.get_dict()      )
                      outfile.write(buffers.unshift(_UpperCamelCase      )      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase      )->			str:
            _A				=       {}

            for i in range(len(_UpperCamelCase      )      ):
                 _A				=       open(filenames[i] ,    'r' ,    _UpperCamelCase      )  # noqa: UP015

            return files






class 					lowerCAmelCase_     :






       def __init__(   self ,    _UpperCamelCase      )->			Union[str, Any]:
            _A				=       block_size






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase=None      )->			Dict:
            _A				=       self.get_number_blocks(_UpperCamelCase ,    self.block_size      )
            _A				=       FileSplitter(_UpperCamelCase      )
            splitter.split(self.block_size ,    _UpperCamelCase      )

            _A				=       FileMerger(NWayMerge()      )
            _A				=       self.block_size / (num_blocks + 1)
            merger.merge(splitter.get_block_filenames() ,    filename + '.out' ,    _UpperCamelCase      )

            splitter.cleanup()






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase      )->			List[str]:
            return (os.stat(_UpperCamelCase      ).st_size / block_size) + 1






def 				lowerCamelCase_   (   __UpperCamelCase				:					Tuple		)				->  Optional[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(__UpperCamelCase		)

def 				lowerCamelCase_   (   )				->  Dict:







     """simple docstring"""
     _A				=       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'		)
     _A				=       parser.parse_args()

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


if __name__ == "__main__":
    main()









'''simple docstring'''
from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

lowerCAmelCase  =				logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):

    def 				lowerCamelCase_   (   cls				:					Tuple		)				->  Dict:







         """simple docstring"""
         return cls

def 				lowerCamelCase_   (   __UpperCamelCase				:					Union[Tuple, List]       ,						__UpperCamelCase				:					Optional[Union[List["torch.Generator"], "torch.Generator"]] = None       ,						__UpperCamelCase				:					Optional["torch.device"] = None       ,						__UpperCamelCase				:					Optional["torch.dtype"] = None       ,						__UpperCamelCase				:					Optional["torch.layout"] = None       ,						)				->  Tuple:







     """simple docstring"""
     _A				=       device
     _A				=       shape[0]

     _A				=       layout or torch.strided
     _A				=       device or torch.device('cpu'		)

     if generator is not None:
          _A				=       generator.device.type if not isinstance(__UpperCamelCase       ,						__UpperCamelCase		) else generator[0].device.type
          if gen_device_type != device.type and gen_device_type == "cpu":
               _A				=       'cpu'
               if device != "mps":
                    logger.info(
                        F'The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'
                        F' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'
                        F' slighly speed up this function by passing a generator that was created on the {device} device.'		)
          elif gen_device_type != device.type and gen_device_type == "cuda":
               raise ValueError(F'Cannot generate a {device} tensor from a generator of type {gen_device_type}.'		)

     if isinstance(__UpperCamelCase       ,						__UpperCamelCase		):
          _A				=       (1,) + shape[1:]
          _A				=       [
              torch.randn(__UpperCamelCase       ,						generator=generator[i]       ,						device=__UpperCamelCase       ,						dtype=__UpperCamelCase       ,						layout=__UpperCamelCase		)
              for i in range(__UpperCamelCase		)
          ]
          _A				=       torch.cat(__UpperCamelCase       ,						dim=0		).to(__UpperCamelCase		)
     else:
          _A				=       torch.randn(__UpperCamelCase       ,						generator=__UpperCamelCase       ,						device=__UpperCamelCase       ,						dtype=__UpperCamelCase       ,						layout=__UpperCamelCase		).to(__UpperCamelCase		)

     return latents

def 				lowerCamelCase_   (   __UpperCamelCase				:					Any		)				->  Optional[int]:







     """simple docstring"""
     if is_torch_version('<'       ,						'2.0.0'		) or not hasattr(__UpperCamelCase       ,						'_dynamo'		):
          return False
     return isinstance(__UpperCamelCase       ,						torch._dynamo.eval_frame.OptimizedModule		)





