




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






class       A      :
     """simple docstring"""

     _snake_case:						List[Any]  ="block_{0}.dat"




     def __init__(     self	: Optional[int]						,	A__	: Optional[Any]      )					->							List[str]:
           lowerCamelCase__   	=					filename
           lowerCamelCase__   	=					[]




     def 			__snake_case (     self	: str						,	A__	: str						,	A__	: Tuple      )					->							Optional[Any]:
           lowerCamelCase__   	=					self.BLOCK_FILENAME_FORMAT.format(A__      )
           with open(A__						,	'w'      ) as file:
                 file.write(A__      )
           self.block_filenames.append(A__      )




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




     def 			__snake_case (     self	: List[str]						,	A__	: List[str]						,	A__	: Any=None      )					->							Optional[Any]:
           lowerCamelCase__   	=					0
           with open(self.filename      ) as file:
                 while True:
                       lowerCamelCase__   	=					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 			__snake_case (     self	: Any      )					->							Optional[Any]:
           map(os.remove						,	self.block_filenames      )






class       A      :
     """simple docstring"""

     def 			__snake_case (     self	: List[Any]						,	A__	: List[Any]      )					->							Union[str, Any]:
           lowerCamelCase__   	=					-1
           lowerCamelCase__   	=					None

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

           return min_index






class       A      :
     """simple docstring"""

     def __init__(     self	: Union[str, Any]						,	A__	: str      )					->							Optional[int]:
           lowerCamelCase__   	=					files
           lowerCamelCase__   	=					set()
           lowerCamelCase__   	=					len(A__      )
           lowerCamelCase__   	=					{i: None for i in range(self.num_buffers      )}




     def 			__snake_case (     self	: str      )					->							Optional[Any]:
           return {
               i: self.buffers[i] for i in range(self.num_buffers      ) if i not in self.empty
           }




     def 			__snake_case (     self	: Any      )					->							List[str]:
           for i in range(self.num_buffers      ):
                 if self.buffers[i] is None and i not in self.empty:
                       lowerCamelCase__   	=					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 			__snake_case (     self	: Tuple						,	A__	: str      )					->							int:
           lowerCamelCase__   	=					self.buffers[index]
           lowerCamelCase__   	=					None

           return value






class       A      :
     """simple docstring"""

     def __init__(     self	: Optional[Any]						,	A__	: Any      )					->							Union[str, Any]:
           lowerCamelCase__   	=					merge_strategy




     def 			__snake_case (     self	: Dict						,	A__	: str						,	A__	: Any						,	A__	: Dict      )					->							Any:
           lowerCamelCase__   	=					FilesArray(self.get_file_handles(A__						,	A__      )      )
           with open(A__						,	'w'						,	A__      ) as outfile:
                 while buffers.refresh():
                       lowerCamelCase__   	=					self.merge_strategy.select(buffers.get_dict()      )
                       outfile.write(buffers.unshift(A__      )      )




     def 			__snake_case (     self	: Tuple						,	A__	: str						,	A__	: Optional[int]      )					->							List[Any]:
           lowerCamelCase__   	=					{}

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

           return files






class       A      :
     """simple docstring"""

     def __init__(     self	: Optional[int]						,	A__	: int      )					->							Optional[int]:
           lowerCamelCase__   	=					block_size




     def 			__snake_case (     self	: str						,	A__	: Optional[Any]						,	A__	: Optional[int]=None      )					->							List[str]:
           lowerCamelCase__   	=					self.get_number_blocks(A__						,	self.block_size      )
           lowerCamelCase__   	=					FileSplitter(A__      )
           splitter.split(self.block_size						,	A__      )

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

           splitter.cleanup()




     def 			__snake_case (     self	: Dict						,	A__	: Optional[Any]						,	A__	: Optional[Any]      )					->							int:
           return (os.stat(A__      ).st_size / block_size) + 1

def       lowerCAmelCase_      (	lowerCamelCase:							Union[str, Any]    )       ->      List[Any]:


      '''simple docstring'''




      if string[-1].lower() == "k":
            return int(string[:-1]    ) * 10_24
      elif string[-1].lower() == "m":
            return int(string[:-1]    ) * 10_24 * 10_24
      elif string[-1].lower() == "g":
            return int(string[:-1]    ) * 10_24 * 10_24 * 10_24
      else:
            return int(lowerCamelCase    )

def       lowerCAmelCase_      (	)       ->      List[Any]:


      '''simple docstring'''




      lowerCamelCase__   	=					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__   	=					parser.parse_args()

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


if __name__ == "__main__":
      main()





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__SCREAMING_SNAKE_CASE     : Dict      =logging.get_logger(__name__)


if is_vision_available():
      import PIL






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:						Any  =["pixel_values"]




     def __init__(     self	: Union[str, Any]						,	A__	: bool = True						,	A__	: Dict[str, int] = None						,	A__	: PILImageResampling = PILImageResampling.BICUBIC						,	A__	: bool = True						,	A__	: Dict[str, int] = None						,	A__	: bool = True						,	A__	: Union[int, float] = 1 / 2_5_5						,	A__	: bool = True						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: bool = True						,	**A__	: Optional[int]						,	)					->							None:
           super().__init__(**A__      )
           lowerCamelCase__   	=					size if size is not None else {'shortest_edge': 2_2_4}
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
           lowerCamelCase__   	=					get_size_dict(A__      )

           lowerCamelCase__   	=					do_resize
           lowerCamelCase__   	=					size
           lowerCamelCase__   	=					resample
           lowerCamelCase__   	=					do_center_crop
           lowerCamelCase__   	=					crop_size
           lowerCamelCase__   	=					do_rescale
           lowerCamelCase__   	=					rescale_factor
           lowerCamelCase__   	=					do_normalize
           lowerCamelCase__   	=					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           lowerCamelCase__   	=					image_std if image_std is not None else OPENAI_CLIP_STD
           lowerCamelCase__   	=					do_convert_rgb




     def 			__snake_case (     self	: Union[str, Any]						,	A__	: np.ndarray						,	A__	: Dict[str, int]						,	A__	: PILImageResampling = PILImageResampling.BICUBIC						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: Optional[int]						,	)					->							np.ndarray:
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					get_resize_output_image_size(
               A__						,	size=(size['height'], size['width'])						,	default_to_square=A__      )
           return resize(A__						,	size=A__						,	resample=A__						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: int						,	A__	: np.ndarray						,	A__	: Dict[str, int]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: List[Any]						,	)					->							np.ndarray:
           lowerCamelCase__   	=					get_size_dict(A__      )
           return center_crop(A__						,	size=(size['height'], size['width'])						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: Dict						,	A__	: np.ndarray						,	A__	: Union[int, float]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: List[Any]						,	)					->							Tuple:
           return rescale(A__						,	scale=A__						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: int						,	A__	: np.ndarray						,	A__	: Union[float, List[float]]						,	A__	: Union[float, List[float]]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: Tuple						,	)					->							np.ndarray:
           return normalize(A__						,	mean=A__						,	std=A__						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: Tuple						,	A__	: ImageInput						,	A__	: bool = None						,	A__	: Dict[str, int] = None						,	A__	: PILImageResampling = None						,	A__	: bool = None						,	A__	: int = None						,	A__	: bool = None						,	A__	: float = None						,	A__	: bool = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: bool = None						,	A__	: Optional[Union[str, TensorType]] = None						,	A__	: Optional[ChannelDimension] = ChannelDimension.FIRST						,	**A__	: List[str]						,	)					->							PIL.Image.Image:
           lowerCamelCase__   	=					do_resize if do_resize is not None else self.do_resize
           lowerCamelCase__   	=					size if size is not None else self.size
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					resample if resample is not None else self.resample
           lowerCamelCase__   	=					do_center_crop if do_center_crop is not None else self.do_center_crop
           lowerCamelCase__   	=					crop_size if crop_size is not None else self.crop_size
           lowerCamelCase__   	=					get_size_dict(A__      )
           lowerCamelCase__   	=					do_rescale if do_rescale is not None else self.do_rescale
           lowerCamelCase__   	=					rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCamelCase__   	=					do_normalize if do_normalize is not None else self.do_normalize
           lowerCamelCase__   	=					image_mean if image_mean is not None else self.image_mean
           lowerCamelCase__   	=					image_std if image_std is not None else self.image_std
           lowerCamelCase__   	=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           lowerCamelCase__   	=					make_list_of_images(A__      )

           if not valid_images(A__      ):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.'      )

           if do_resize and size is None:
                 raise ValueError('Size must be specified if do_resize is True.'      )

           if do_center_crop and crop_size is None:
                 raise ValueError('Crop size must be specified if do_center_crop is True.'      )

           if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                 lowerCamelCase__   	=					[convert_to_rgb(A__      ) for image in images]

           # All transformations expect numpy arrays.
           lowerCamelCase__   	=					[to_numpy_array(A__      ) for image in images]

           if do_resize:
                 lowerCamelCase__   	=					[self.resize(image=A__						,	size=A__						,	resample=A__      ) for image in images]

           if do_center_crop:
                 lowerCamelCase__   	=					[self.center_crop(image=A__						,	size=A__      ) for image in images]

           if do_rescale:
                 lowerCamelCase__   	=					[self.rescale(image=A__						,	scale=A__      ) for image in images]

           if do_normalize:
                 lowerCamelCase__   	=					[self.normalize(image=A__						,	mean=A__						,	std=A__      ) for image in images]

           lowerCamelCase__   	=					[to_channel_dimension_format(A__						,	A__      ) for image in images]

           lowerCamelCase__   	=					{'pixel_values': images}
           return BatchFeature(data=A__						,	tensor_type=A__      )