



"""simple docstring"""







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





class   UpperCamelCase	:




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  "block_{0}.dat"






	def __init__(     self				,UpperCAmelCase_   ):
			_lowercase				: Dict     =   filename
			_lowercase				: Union[str, Any]     =   []






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: Tuple     =   self.BLOCK_FILENAME_FORMAT.format(UpperCAmelCase_   )
			with open(UpperCAmelCase_				,"""w"""   ) as file:
					file.write(UpperCAmelCase_   )
			self.block_filenames.append(UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			return self.block_filenames






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None   ):
			_lowercase				: int     =   0
			with open(self.filename   ) as file:
					while True:
							_lowercase				: Optional[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    lowerCamelCase__					(     self   ):
			map(os.remove				,self.block_filenames   )







class   UpperCamelCase	:




	"""simple docstring"""






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: Any     =   -1
			_lowercase				: int     =   None

			for i in range(len(UpperCAmelCase_   )   ):
					if min_str is None or choices[i] < min_str:
							_lowercase				: List[str]     =   i

			return min_index







class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_   ):
			_lowercase				: int     =   files
			_lowercase				: List[Any]     =   set()
			_lowercase				: List[Any]     =   len(UpperCAmelCase_   )
			_lowercase				: Tuple     =   {i: None for i in range(self.num_buffers   )}






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






	def    lowerCamelCase__					(     self   ):
			for i in range(self.num_buffers   ):
					if self.buffers[i] is None and i not in self.empty:
							_lowercase				: Optional[int]     =   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    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: List[str]     =   self.buffers[index]
			_lowercase				: Optional[Any]     =   None

			return value







class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_   ):
			_lowercase				: Optional[int]     =   merge_strategy






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: List[str]     =   FilesArray(self.get_file_handles(UpperCAmelCase_				,UpperCAmelCase_   )   )
			with open(UpperCAmelCase_				,"""w"""				,UpperCAmelCase_   ) as outfile:
					while buffers.refresh():
							_lowercase				: Tuple     =   self.merge_strategy.select(buffers.get_dict()   )
							outfile.write(buffers.unshift(UpperCAmelCase_   )   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: Tuple     =   {}

			for i in range(len(UpperCAmelCase_   )   ):
					_lowercase				: str     =   open(filenames[i]				,"""r"""				,UpperCAmelCase_   )  # noqa: UP015

			return files







class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_   ):
			_lowercase				: Any     =   block_size






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None   ):
			_lowercase				: Tuple     =   self.get_number_blocks(UpperCAmelCase_				,self.block_size   )
			_lowercase				: Union[str, Any]     =   FileSplitter(UpperCAmelCase_   )
			splitter.split(self.block_size				,UpperCAmelCase_   )

			_lowercase				: str     =   FileMerger(NWayMerge()   )
			_lowercase				: Tuple     =   self.block_size / (num_blocks + 1)
			merger.merge(splitter.get_block_filenames()				,filename + """.out"""				,UpperCAmelCase_   )

			splitter.cleanup()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):
			return (os.stat(UpperCAmelCase_   ).st_size / block_size) + 1







def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		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(__UpperCAmelCase      )


def  __SCREAMING_SNAKE_CASE (  ):
		_lowercase				: List[str]     =   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				: Optional[int]     =   parser.parse_args()

		_lowercase				: Tuple     =   ExternalSort(parse_memory(args.mem      )      )
		sorter.sort(args.filename[0]      )


if __name__ == "__main__":
		main()




'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=sys.maxsize					):
									snake_case :       Any				=    "bilinear"
									snake_case :       Dict				=    max_size
									snake_case :       Dict				=    short_edge_length






			def __call__(							self				,			snake_case_					):
									snake_case :       Optional[Any]				=    []
									for img in imgs:
															snake_case				,					snake_case :       Dict				=    img.shape[:2]
															# later: provide list and randomly choose index for resize
															snake_case :       Dict				=    np.random.randint(self.short_edge_length[0]				,			self.short_edge_length[1] + 1					)
															if size == 0:
																					return img
															snake_case :       Any				=    size * 1.0 / min(snake_case_				,			snake_case_					)
															if h < w:
																					snake_case				,					snake_case :       Optional[int]				=    size, scale * w
															else:
																					snake_case				,					snake_case :       Tuple				=    scale * h, size
															if max(snake_case_				,			snake_case_					) > self.max_size:
																					snake_case :       Optional[int]				=    self.max_size * 1.0 / max(snake_case_				,			snake_case_					)
																					snake_case :       Dict				=    newh * scale
																					snake_case :       List[Any]				=    neww * scale
															snake_case :       int				=    int(neww + 0.5					)
															snake_case :       List[Any]				=    int(newh + 0.5					)

															if img.dtype == np.uinta:
																					snake_case :       List[Any]				=    Image.fromarray(snake_case_					)
																					snake_case :       str				=    pil_image.resize((neww, newh)				,			PILImageResampling.BILINEAR					)
																					snake_case :       List[str]				=    np.asarray(snake_case_					)
															else:
																					snake_case :       List[Any]				=    img.permute(2				,			0				,			1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
																					snake_case :       Tuple				=    nn.functional.interpolate(
																					    snake_case_				,			(newh, neww)				,			mode=self.interp_method				,			align_corners=snake_case_					).squeeze(0					)
															img_augs.append(snake_case_					)

									return img_augs







class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,			cfg.INPUT.MAX_SIZE_TEST					)
									snake_case :       Any				=    cfg.INPUT.FORMAT
									snake_case :       Optional[int]				=    cfg.SIZE_DIVISIBILITY
									snake_case :       Optional[int]				=    cfg.PAD_VALUE
									snake_case :       int				=    cfg.INPUT.MAX_SIZE_TEST
									snake_case :       Optional[int]				=    cfg.MODEL.DEVICE
									snake_case :       int				=    torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       List[Any]				=    torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       Dict				=    lambda snake_case_					: (x - self.pixel_mean) / self.pixel_std



			def   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       List[Any]				=    tuple(max(snake_case_					) for s in zip(*[img.shape for img in images]					)					)
									snake_case :       int				=    [im.shape[-2:] for im in images]
									snake_case :       List[Any]				=    [
									    nn.functional.pad(
									        snake_case_				,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,			value=self.pad_value				,			)
									    for size, im in zip(snake_case_				,			snake_case_					)
									]

									return torch.stack(snake_case_					), torch.tensor(snake_case_					)






			def __call__(							self				,			snake_case_				,			snake_case_=False					):
									with torch.no_grad():
															if not isinstance(snake_case_				,			snake_case_					):
																					snake_case :       int				=    [images]
															if single_image:
																					assert len(snake_case_					) == 1
															for i in range(len(snake_case_					)					):
																					if isinstance(images[i]				,			torch.Tensor					):
																											images.insert(snake_case_				,			images.pop(snake_case_					).to(self.device					).float()					)
																					elif not isinstance(images[i]				,			torch.Tensor					):
																											images.insert(
																											    snake_case_				,			torch.as_tensor(img_tensorize(images.pop(snake_case_					)				,			input_format=self.input_format					)					)
																											    .to(self.device					)
																											    .float()				,			)
            # resize smallest edge
															snake_case :       List[Any]				=    torch.tensor([im.shape[:2] for im in images]					)
															snake_case :       Any				=    self.aug(snake_case_					)
															# transpose images and convert to torch tensors
															# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
															# now normalize before pad to avoid useless arithmetic
															snake_case :       str				=    [self.normalizer(snake_case_					) for x in images]
															# now pad them to do the following operations
															snake_case				,					snake_case :       int				=    self.pad(snake_case_					)
															# Normalize

															if self.size_divisibility > 0:
																					raise NotImplementedError()
															# pad
															snake_case :       Union[str, Any]				=    torch.true_divide(snake_case_				,			snake_case_					)
															if single_image:
																					return images[0], sizes[0], scales_yx[0]
															else:
																					return images, sizes, scales_yx







def        UpperCAmelCase__   (				_lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Optional[int])    ->		int:



						"""simple docstring"""

						boxes[:, 0::2] *= scale_yx[:, 1]
						boxes[:, 1::2] *= scale_yx[:, 0]
						return boxes



def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Tuple[int, int])    ->		List[str]:



						"""simple docstring"""

						assert torch.isfinite(_lowerCAmelCase).all(), "Box tensor contains infinite or NaN!"
						snake_case				,					snake_case :       Union[str, Any]				=    box_size
						tensor[:, 0].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 1].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 2].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 3].clamp_(min=0,      max=_lowerCAmelCase)





