



'''simple docstring'''

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



class 		lowerCamelCase__   :




					'''simple docstring'''



					A_         =    """block_{0}.dat"""




					def __init__(	self					:						int  ,	UpperCamelCase_					:						Dict       )	->							Any:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Union[str, Any]  ,	UpperCamelCase_					:						Optional[Any]       )	->							str:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						List[str]       )	->							Dict:



						'''simple docstring'''





						return self.block_filenames




					def     __UpperCAmelCase  (	self					:						Any  ,	UpperCamelCase_					:						Optional[Any]  ,	UpperCamelCase_					:						Tuple=None       )	->							Dict:



						'''simple docstring'''





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



						'''simple docstring'''





						map(os.remove  ,	self.block_filenames       )






class 		lowerCamelCase__   :




					'''simple docstring'''



					def     __UpperCAmelCase  (	self					:						str  ,	UpperCamelCase_					:						Optional[int]       )	->							int:



						'''simple docstring'''





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

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

						return min_index






class 		lowerCamelCase__   :




					'''simple docstring'''



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



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						str       )	->							Tuple:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						Tuple       )	->							Any:



						'''simple docstring'''





						for i in range(self.num_buffers       ):
							if self.buffers[i] is None and i not in self.empty:
								_lowercase       :							Dict  	=		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					:						Tuple  ,	UpperCamelCase_					:						Optional[int]       )	->							List[Any]:



						'''simple docstring'''





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

						return value






class 		lowerCamelCase__   :




					'''simple docstring'''



					def __init__(	self					:						str  ,	UpperCamelCase_					:						int       )	->							int:



						'''simple docstring'''





						_lowercase       :							Union[str, Any]  	=		merge_strategy




					def     __UpperCAmelCase  (	self					:						Dict  ,	UpperCamelCase_					:						str  ,	UpperCamelCase_					:						List[Any]  ,	UpperCamelCase_					:						Union[str, Any]       )	->							Optional[Any]:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						Any  ,	UpperCamelCase_					:						List[str]       )	->							int:



						'''simple docstring'''





						_lowercase       :							List[Any]  	=		{}

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

						return files






class 		lowerCamelCase__   :




					'''simple docstring'''



					def __init__(	self					:						Any  ,	UpperCamelCase_					:						Optional[int]       )	->							Optional[Any]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		block_size




					def     __UpperCAmelCase  (	self					:						Tuple  ,	UpperCamelCase_					:						Optional[int]  ,	UpperCamelCase_					:						Dict=None       )	->							str:



						'''simple docstring'''





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

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

						splitter.cleanup()




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						Dict  ,	UpperCamelCase_					:						Optional[int]       )	->							Optional[Any]:



						'''simple docstring'''





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







def     __UpperCamelCase					(				_lowercase       )			->      Tuple:
	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(SCREAMING_SNAKE_CASE_       )







def     __UpperCamelCase					(				)			->      List[Any]:
	_lowercase       :							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'       )
	_lowercase       :							str  	=		parser.parse_args()

	_lowercase       :							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  )