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







class 	A__				:
				lowercase            =    'block_{0}.dat'






				def __init__(		self   : int       ,	a   : Dict  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						filename
							lowerCAmelCase__ : str   =						[]






				def 				_lowerCamelCase						(		self   : Optional[int]       ,	a   : Union[str, Any]       ,	a   : Optional[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[Any]   =						self.BLOCK_FILENAME_FORMAT.format(a  )
							with open(a       ,	'w'  ) as file:
										file.write(a  )
							self.block_filenames.append(a  )






				def 				_lowerCamelCase						(		self   : List[str]  ):






							'''simple docstring'''







							return self.block_filenames






				def 				_lowerCamelCase						(		self   : Any       ,	a   : Optional[Any]       ,	a   : Tuple=None  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						0
							with open(self.filename  ) as file:
										while True:
													lowerCAmelCase__ : List[Any]   =						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   : Any  ):






							'''simple docstring'''







							map(os.remove       ,	self.block_filenames  )







class 	A__				:






				def 				_lowerCamelCase						(		self   : str       ,	a   : Optional[int]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						-1
							lowerCAmelCase__ : List[Any]   =						None

							for i in range(len(a  )  ):
										if min_str is None or choices[i] < min_str:
													lowerCAmelCase__ : Tuple   =						i

							return min_index







class 	A__				:






				def __init__(		self   : str       ,	a   : List[str]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Tuple   =						files
							lowerCAmelCase__ : Tuple   =						set()
							lowerCAmelCase__ : Tuple   =						len(a  )
							lowerCAmelCase__ : Tuple   =						{i: None for i in range(self.num_buffers  )}






				def 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							for i in range(self.num_buffers  ):
										if self.buffers[i] is None and i not in self.empty:
													lowerCAmelCase__ : Dict   =						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   : Tuple       ,	a   : Optional[int]  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[str]   =						self.buffers[index]
							lowerCAmelCase__ : List[Any]   =						None

							return value







class 	A__				:






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






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						merge_strategy






				def 				_lowerCamelCase						(		self   : Dict       ,	a   : str       ,	a   : List[Any]       ,	a   : Union[str, Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						FilesArray(self.get_file_handles(a       ,	a  )  )
							with open(a       ,	'w'       ,	a  ) as outfile:
										while buffers.refresh():
													lowerCAmelCase__ : str   =						self.merge_strategy.select(buffers.get_dict()  )
													outfile.write(buffers.unshift(a  )  )






				def 				_lowerCamelCase						(		self   : List[Any]       ,	a   : Any       ,	a   : List[str]  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						{}

							for i in range(len(a  )  ):
										lowerCAmelCase__ : List[Any]   =						open(filenames[i]       ,	'r'       ,	a  )  # noqa: UP015

							return files







class 	A__				:






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






							'''simple docstring'''







							lowerCAmelCase__ : Dict   =						block_size






				def 				_lowerCamelCase						(		self   : Tuple       ,	a   : Optional[int]       ,	a   : Dict=None  ):






							'''simple docstring'''







							lowerCAmelCase__ : Dict   =						self.get_number_blocks(a       ,	self.block_size  )
							lowerCAmelCase__ : Union[str, Any]   =						FileSplitter(a  )
							splitter.split(self.block_size       ,	a  )

							lowerCAmelCase__ : List[str]   =						FileMerger(NWayMerge()  )
							lowerCAmelCase__ : List[Any]   =						self.block_size / (num_blocks + 1)
							merger.merge(splitter.get_block_filenames()       ,	filename + '.out'       ,	a  )

							splitter.cleanup()






				def 				_lowerCamelCase						(		self   : List[Any]       ,	a   : Dict       ,	a   : Optional[int]  ):






							'''simple docstring'''







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






def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Tuple:
			if string[-1].lower() == "k":
						return int(string[:-1]	) * 1_024
			elif string[-1].lower() == "m":
						return int(string[:-1]	) * 1_024 * 1_024
			elif string[-1].lower() == "g":
						return int(string[:-1]	) * 1_024 * 1_024 * 1_024
			else:
						return int(SCREAMING_SNAKE_CASE_	)
def 					lowerCAmelCase__     (    )							->						List[Any]:
			lowerCAmelCase__ : Dict   =						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'	)
			lowerCAmelCase__ : str   =						parser.parse_args()

			lowerCAmelCase__ : Dict   =						ExternalSort(parse_memory(args.mem	)	)
			sorter.sort(args.filename[0]	)


if __name__ == "__main__":
					main()

from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 	A__				(	__magic_name__      ):






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






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a       ,	scheduler=a  )






				@torch.no_grad()
				def __call__(		self   : Optional[Any]       ,	a   : int = 1       ,	a   : Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,	a   : int = 1_000       ,	a   : Optional[str] = "pil"       ,	a   : bool = True       ,	):






							'''simple docstring'''







							if isinstance(self.unet.config.sample_size       ,	a  ):
										lowerCAmelCase__ : Union[str, Any]   =						(
										    batch_size,
										    self.unet.config.in_channels,
										    self.unet.config.sample_size,
										    self.unet.config.sample_size,
										)
							else:
										lowerCAmelCase__ : str   =						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if self.device.type == "mps":
										# randn does not work reproducibly on mps
										lowerCAmelCase__ : Optional[int]   =						randn_tensor(a       ,	generator=a  )
										lowerCAmelCase__ : Optional[int]   =						image.to(self.device  )
							else:
										lowerCAmelCase__ : Dict   =						randn_tensor(a       ,	generator=a       ,	device=self.device  )

							# set step values
							self.scheduler.set_timesteps(a  )

							for t in self.progress_bar(self.scheduler.timesteps  ):
										# 1. predict noise model_output
										lowerCAmelCase__ : Tuple   =						self.unet(a       ,	a  ).sample

										# 2. compute previous image: x_t -> x_t-1
										lowerCAmelCase__ : Optional[Any]   =						self.scheduler.step(a       ,	a       ,	a       ,	generator=a  ).prev_sample

							lowerCAmelCase__ : str   =						(image / 2 + 0.5).clamp(0       ,	1  )
							lowerCAmelCase__ : Optional[int]   =						image.cpu().permute(0       ,	2       ,	3       ,	1  ).numpy()
							if output_type == "pil":
										lowerCAmelCase__ : int   =						self.numpy_to_pil(a  )

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=a  )