






"""simple docstring"""







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







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     ='block_{0}.dat'




							def __init__( self:  Tuple					,		__A:  Union[str, Any]     ):

												'''simple docstring'''

												a__ 							=	filename
												a__ 							=	[]




							def 			lowercase		( self:  Union[str, Any]					,		__A:  List[str]					,		__A:  List[str]     ):

												'''simple docstring'''

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




							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

												return self.block_filenames




							def 			lowercase		( self:  Dict					,		__A:  Tuple					,		__A:  Optional[Any]=None     ):

												'''simple docstring'''

												a__ 							=	0
												with open(self.filename     ) as file:
																	while True:
																						a__ 							=	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 			lowercase		( self:  Any     ):

												'''simple docstring'''

												map(os.remove					,		self.block_filenames     )







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def 			lowercase		( self:  Optional[int]					,		__A:  str     ):

												'''simple docstring'''

												a__ 							=	-1
												a__ 							=	None

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

												return min_index







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  int					,		__A:  str     ):

												'''simple docstring'''

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




							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

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




							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												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(__A     )
																											self.files[i].close()

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

												return True





							def 			lowercase		( self:  List[Any]					,		__A:  Optional[Any]     ):

												'''simple docstring'''

												a__ 							=	self.buffers[index]
												a__ 							=	None

												return value







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  Union[str, Any]					,		__A:  Any     ):

												'''simple docstring'''

												a__ 							=	merge_strategy




							def 			lowercase		( self:  Any					,		__A:  Optional[int]					,		__A:  str					,		__A:  Optional[int]     ):

												'''simple docstring'''

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





							def 			lowercase		( self:  Union[str, Any]					,		__A:  Optional[Any]					,		__A:  Optional[int]     ):

												'''simple docstring'''

												a__ 							=	{}

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

												return files







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  Optional[Any]					,		__A:  int     ):

												'''simple docstring'''

												a__ 							=	block_size




							def 			lowercase		( self:  Optional[Any]					,		__A:  Dict					,		__A:  Any=None     ):

												'''simple docstring'''

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

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

												splitter.cleanup()





							def 			lowercase		( self:  int					,		__A:  Any					,		__A:  str     ):

												'''simple docstring'''

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





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					if string[-1].lower() == "k":
										return int(string[:-1]) * 1024
					elif string[-1].lower() == "m":
										return int(string[:-1]) * 1024 * 1024
					elif string[-1].lower() == "g":
										return int(string[:-1]) * 1024 * 1024 * 1024
					else:
										return int(lowerCamelCase_)





def 							SCREAMING_SNAKE_CASE							(   ):
					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"""







# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
	import tensorflow as tf







class 							_SCREAMING_SNAKE_CASE    (	TensorFormatter[Mapping, 'tf.Tensor', Mapping]				):





							"""simple docstring"""





							def __init__( self:  Union[str, Any]					,		__A:  Union[str, Any]=None					,		**__A:  Union[str, Any]     ):

												'''simple docstring'''

												super().__init__(features=__A     )
												a__ 							=	tf_tensor_kwargs
												import tensorflow as tf  # noqa: import tf at initialization




							def 			lowercase		( self:  Optional[int]					,		__A:  List[Any]     ):

												'''simple docstring'''

												import tensorflow as tf

												if isinstance(__A					,		__A     ) and column:
																	if all(
																	    isinstance(__A					,		tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
																						return tf.stack(__A     )
																	elif all(
																	    isinstance(__A					,		(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
																	    for x in column     ):
																						# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																						return tf.ragged.stack(__A     )

												return column




							def 			lowercase		( self:  int					,		__A:  Optional[Any]     ):

												'''simple docstring'''

												import tensorflow as tf

												if value is None:
																	return value

												a__ 							=	{}

												if isinstance(__A					,		(np.number, np.ndarray)     ) and np.issubdtype(value.dtype					,		np.integer     ):
																	a__ 							=	{'''dtype''': tf.intaa}
												elif isinstance(__A					,		(np.number, np.ndarray)     ) and np.issubdtype(value.dtype					,		np.floating     ):
																	a__ 							=	{'''dtype''': tf.floataa}
												elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																	import PIL.Image

																	if isinstance(__A					,		PIL.Image.Image     ):
																						a__ 							=	np.asarray(__A     )

												return tf.convert_to_tensor(__A					,		**{**default_dtype, **self.tf_tensor_kwargs}     )




							def 			lowercase		( self:  List[Any]					,		__A:  str     ):

												'''simple docstring'''

												import tensorflow as tf

												# support for torch, tf, jax etc.
												if config.TORCH_AVAILABLE and "torch" in sys.modules:
																	import torch

																	if isinstance(__A					,		torch.Tensor     ):
																						return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
												if hasattr(__A					,		'''__array__'''     ) and not isinstance(__A					,		tf.Tensor     ):
																	a__ 							=	data_struct.__array__()
												# support for nested types like struct of list of struct
												if isinstance(__A					,		np.ndarray     ):
																	if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																						return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
												elif isinstance(__A					,		(list, tuple)     ):
																	return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
												return self._tensorize(__A     )




							def 			lowercase		( self:  int					,		__A:  dict     ):

												'''simple docstring'''

												return map_nested(self._recursive_tensorize					,		__A					,		map_list=__A     )




							def 			lowercase		( self:  Tuple					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_row(__A     )
												a__ 							=	self.python_features_decoder.decode_row(__A     )
												return self.recursive_tensorize(__A     )




							def 			lowercase		( self:  Dict					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_column(__A     )
												a__ 							=	self.python_features_decoder.decode_column(__A					,		pa_table.column_names[0]     )
												a__ 							=	self.recursive_tensorize(__A     )
												a__ 							=	self._consolidate(__A     )
												return column





							def 			lowercase		( self:  Tuple					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_batch(__A     )
												a__ 							=	self.python_features_decoder.decode_batch(__A     )
												a__ 							=	self.recursive_tensorize(__A     )
												for column_name in batch:
																	a__ 							=	self._consolidate(batch[column_name]     )
												return batch


