





'''simple docstring'''







import argparse
import os


class    __a :
							__a   :				Union[str, Any]       =    "block_{0}.dat"

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







													"""simple docstring"""
													UpperCAmelCase_     : Optional[int]         =					filename
													UpperCAmelCase_     : str         =					[]

							def       UpperCAmelCase__		(							self							:       int    ,       __magic_name__							:       int    ,       __magic_name__							:       str				)   ->    Tuple:







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					self.BLOCK_FILENAME_FORMAT.format(__magic_name__				)
													with open(__magic_name__    ,       '''w'''				) as file:
																			file.write(__magic_name__				)
													self.block_filenames.append(__magic_name__				)

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







													"""simple docstring"""
													return self.block_filenames

							def       UpperCAmelCase__		(							self							:       List[str]    ,       __magic_name__							:       Dict    ,       __magic_name__							:       Dict=None				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : Any         =					0
													with open(self.filename				) as file:
																			while True:
																									UpperCAmelCase_     : List[Any]         =					file.readlines(__magic_name__				)

																									if lines == []:
																															break

																									if sort_key is None:
																															lines.sort()
																									else:
																															lines.sort(key=__magic_name__				)

																									self.write_block(''''''.join(__magic_name__				)    ,       __magic_name__				)
																									i += 1



							def       UpperCAmelCase__		(							self							:       str				)   ->    Optional[int]:







													"""simple docstring"""
													map(os.remove    ,       self.block_filenames				)





class    __a :

							def       UpperCAmelCase__		(							self							:       List[Any]    ,       __magic_name__							:       str				)   ->    int:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					-1
													UpperCAmelCase_     : Optional[int]         =					None

													for i in range(len(__magic_name__				)				):
																			if min_str is None or choices[i] < min_str:
																									UpperCAmelCase_     : int         =					i

													return min_index





class    __a :

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







													"""simple docstring"""
													UpperCAmelCase_     : Union[str, Any]         =					files
													UpperCAmelCase_     : int         =					set()
													UpperCAmelCase_     : int         =					len(__magic_name__				)
													UpperCAmelCase_     : Dict         =					{i: None for i in range(self.num_buffers				)}

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







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

							def       UpperCAmelCase__		(							self							:       int				)   ->    Any:







													"""simple docstring"""
													for i in range(self.num_buffers				):
																			if self.buffers[i] is None and i not in self.empty:
																									UpperCAmelCase_     : Optional[Any]         =					self.files[i].readline()

																									if self.buffers[i] == "":
																															self.empty.add(__magic_name__				)
																															self.files[i].close()

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

													return True



							def       UpperCAmelCase__		(							self							:       Tuple    ,       __magic_name__							:       Dict				)   ->    List[str]:







													"""simple docstring"""
													UpperCAmelCase_     : Union[str, Any]         =					self.buffers[index]
													UpperCAmelCase_     : str         =					None

													return value





class    __a :

							def __init__(							self							:       Optional[int]    ,       __magic_name__							:       str				)   ->    Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					merge_strategy

							def       UpperCAmelCase__		(							self							:       str    ,       __magic_name__							:       int    ,       __magic_name__							:       Dict    ,       __magic_name__							:       Optional[int]				)   ->    str:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[int]         =					FilesArray(self.get_file_handles(__magic_name__    ,       __magic_name__				)				)
													with open(__magic_name__    ,       '''w'''    ,       __magic_name__				) as outfile:
																			while buffers.refresh():
																									UpperCAmelCase_     : Union[str, Any]         =					self.merge_strategy.select(buffers.get_dict()				)
																									outfile.write(buffers.unshift(__magic_name__				)				)



							def       UpperCAmelCase__		(							self							:       Optional[Any]    ,       __magic_name__							:       Optional[Any]    ,       __magic_name__							:       int				)   ->    Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					{}

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

													return files





class    __a :

							def __init__(							self							:       List[Any]    ,       __magic_name__							:       Tuple				)   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : Tuple         =					block_size

							def       UpperCAmelCase__		(							self							:       List[Any]    ,       __magic_name__							:       Optional[int]    ,       __magic_name__							:       Union[str, Any]=None				)   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[Any]         =					self.get_number_blocks(__magic_name__    ,       self.block_size				)
													UpperCAmelCase_     : List[str]         =					FileSplitter(__magic_name__				)
													splitter.split(self.block_size    ,       __magic_name__				)

													UpperCAmelCase_     : int         =					FileMerger(NWayMerge()				)
													UpperCAmelCase_     : Dict         =					self.block_size / (num_blocks + 1)
													merger.merge(splitter.get_block_filenames()    ,       filename + '''.out'''    ,       __magic_name__				)

													splitter.cleanup()



							def       UpperCAmelCase__		(							self							:       int    ,       __magic_name__							:       str    ,       __magic_name__							:       Tuple				)   ->    int:







													"""simple docstring"""
													return (os.stat(__magic_name__				).st_size / block_size) + 1





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     Optional[int]				)	->		List[Any]:
						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     lowerCamelCase_ (					)	->		List[str]:
						UpperCAmelCase_     : List[Any]         =					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'''				)
						UpperCAmelCase_     : str         =					parser.parse_args()

						UpperCAmelCase_     : Optional[Any]         =					ExternalSort(parse_memory(args.mem				)				)
						sorter.sort(args.filename[0]				)


if __name__ == "__main__":
					main()








'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor


class    __a (unittest.TestCase       ):

							def __init__(							self							:       Dict    ,       __magic_name__							:       List[str]    ,       __magic_name__							:       str=7    ,       __magic_name__							:       Optional[int]=3    ,       __magic_name__							:       Union[str, Any]=18    ,       __magic_name__							:       Dict=30    ,       __magic_name__							:       List[Any]=4_00    ,       __magic_name__							:       Dict=True    ,       __magic_name__							:       Union[str, Any]=None    ,       __magic_name__							:       Optional[Any]=0.8_7_5    ,       __magic_name__							:       List[Any]=True    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       )   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					size if size is not None else {'''shortest_edge''': 20}
													UpperCAmelCase_     : Optional[Any]         =					parent
													UpperCAmelCase_     : Any         =					batch_size
													UpperCAmelCase_     : Optional[Any]         =					num_channels
													UpperCAmelCase_     : Optional[Any]         =					image_size
													UpperCAmelCase_     : Optional[int]         =					min_resolution
													UpperCAmelCase_     : Optional[Any]         =					max_resolution
													UpperCAmelCase_     : Optional[int]         =					do_resize
													UpperCAmelCase_     : Optional[Any]         =					size
													UpperCAmelCase_     : int         =					crop_pct
													UpperCAmelCase_     : Optional[int]         =					do_normalize
													UpperCAmelCase_     : Dict         =					image_mean
													UpperCAmelCase_     : int         =					image_std



							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    int:







													"""simple docstring"""
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "crop_pct": self.crop_pct,
													}





@require_torch
@require_vision
class    __a (lowerCamelCase    ,						unittest.TestCase       ):
							__a   :				Dict       =    ConvNextImageProcessor if is_vision_available() else None

							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					ConvNextImageProcessingTester(self				)

							@property
							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    Any:







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    Tuple:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_resize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''size'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''crop_pct'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_normalize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_mean'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_std'''				)				)

							def       UpperCAmelCase__		(							self							:       Dict				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					self.image_processing_class.from_dict(self.image_processor_dict				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 20}				)

													UpperCAmelCase_     : List[Any]         =					self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 42}				)

							def       UpperCAmelCase__		(							self							:       str				)   ->    Optional[int]:







													"""simple docstring"""
													pass

							def       UpperCAmelCase__		(							self							:       List[str]				)   ->    int:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : int         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       Image.Image				)

													# Test not batched input
													UpperCAmelCase_     : int         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Tuple         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

							def       UpperCAmelCase__		(							self							:       str				)   ->    Optional[int]:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Optional[Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random numpy tensors
													UpperCAmelCase_     : Tuple         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       numpify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       np.ndarray				)

													# Test not batched input
													UpperCAmelCase_     : int         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Union[str, Any]         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )



							def       UpperCAmelCase__		(							self							:       str				)   ->    int:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Union[str, Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       torchify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       torch.Tensor				)

													# Test not batched input
													UpperCAmelCase_     : List[Any]         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : str         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )





