



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 ViltImageProcessor

class 			__snake_case   ( unittest.TestCase):







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_=7	,	a_=3	,	a_=18	,	a_=30	,	a_=400	,	a_=True	,	a_=None	,	a_=2	,	a_=True	,	a_=[0.5, 0.5, 0.5]	,	a_=[0.5, 0.5, 0.5]	,	):
											a__					   =		size if size is not None else {"""shortest_edge""": 30}
											a__					   =		parent
											a__					   =		batch_size
											a__					   =		num_channels
											a__					   =		image_size
											a__					   =		min_resolution
											a__					   =		max_resolution
											a__					   =		do_resize
											a__					   =		size
											a__					   =		size_divisor
											a__					   =		do_normalize
											a__					   =		image_mean
											a__					   =		image_std


							def _a   (				self		):
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "size_divisor": self.size_divisor,
											}







							def _a   (				self	,	a_	,	a_=False		):
											if not batched:
															a__					   =		self.size["""shortest_edge"""]
															a__					   =		image_inputs[0]
															if isinstance(a_	,	Image.Image		):
																			a__   ,      a__					   =		image.size
															else:
																			a__   ,      a__					   =		image.shape[1], image.shape[2]
															a__					   =		size / min(a_	,	a_		)
															if h < w:
																			a__   ,      a__					   =		size, scale * w
															else:
																			a__   ,      a__					   =		scale * h, size

															a__					   =		int((1_333 / 800) * size		)
															if max(a_	,	a_		) > max_size:
																			a__					   =		max_size / max(a_	,	a_		)
																			a__					   =		newh * scale
																			a__					   =		neww * scale

															a__   ,      a__					   =		int(newh + 0.5		), int(neww + 0.5		)
															a__   ,      a__					   =		(
															    newh // self.size_divisor * self.size_divisor,
															    neww // self.size_divisor * self.size_divisor,
															)

											else:
															a__					   =		[]
															for image in image_inputs:
																			a__   ,      a__					   =		self.get_expected_values([image]		)
																			expected_values.append((expected_height, expected_width)		)
															a__					   =		max(a_	,	key=lambda a_		: item[0]		)[0]
															a__					   =		max(a_	,	key=lambda a_		: item[1]		)[1]

											return expected_height, expected_width




@require_torch
@require_vision
class 			__snake_case   ( SCREAMING_SNAKE_CASE      ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__					:   List[str]       =       ViltImageProcessor if is_vision_available() else None


							def _a   (				self		):
											a__					   =		ViltImageProcessingTester(self		)


							@property
							def _a   (				self		):
											return self.image_processor_tester.prepare_image_processor_dict()


							def _a   (				self		):
											a__					   =		self.image_processing_class(**self.image_processor_dict		)
											self.assertTrue(hasattr(a_	,	"""image_mean"""		)		)
											self.assertTrue(hasattr(a_	,	"""image_std"""		)		)
											self.assertTrue(hasattr(a_	,	"""do_normalize"""		)		)
											self.assertTrue(hasattr(a_	,	"""do_resize"""		)		)
											self.assertTrue(hasattr(a_	,	"""size"""		)		)
											self.assertTrue(hasattr(a_	,	"""size_divisor"""		)		)


							def _a   (				self		):
											a__					   =		self.image_processing_class.from_dict(self.image_processor_dict		)
											self.assertEqual(image_processor.size	,	{"""shortest_edge""": 30}		)

											a__					   =		self.image_processing_class.from_dict(self.image_processor_dict	,	size=42		)
											self.assertEqual(image_processor.size	,	{"""shortest_edge""": 42}		)


							def _a   (				self		):
											pass


							def _a   (				self		):
											# Initialize image_processing
											a__					   =		self.image_processing_class(**self.image_processor_dict		)
											# create random PIL images
											a__					   =		prepare_image_inputs(self.image_processor_tester	,	equal_resolution=a_		)
											for image in image_inputs:
															self.assertIsInstance(a_	,	Image.Image		)

											# Test not batched input
											a__					   =		image_processing(image_inputs[0]	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_		)
											self.assertEqual(
											    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

											# Test batched
											a__					   =		image_processing(a_	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_	,	batched=a_		)
											self.assertEqual(
											    encoded_images.shape	,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )	,	)


							def _a   (				self		):
											# Initialize image_processing
											a__					   =		self.image_processing_class(**self.image_processor_dict		)
											# create random numpy tensors
											a__					   =		prepare_image_inputs(self.image_processor_tester	,	equal_resolution=a_	,	numpify=a_		)
											for image in image_inputs:
															self.assertIsInstance(a_	,	np.ndarray		)

											# Test not batched input
											a__					   =		image_processing(image_inputs[0]	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_		)
											self.assertEqual(
											    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

											# Test batched
											a__					   =		image_processing(a_	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_	,	batched=a_		)
											self.assertEqual(
											    encoded_images.shape	,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )	,	)







							def _a   (				self		):
											# Initialize image_processing
											a__					   =		self.image_processing_class(**self.image_processor_dict		)
											# create random PyTorch tensors
											a__					   =		prepare_image_inputs(self.image_processor_tester	,	equal_resolution=a_	,	torchify=a_		)
											for image in image_inputs:
															self.assertIsInstance(a_	,	torch.Tensor		)

											# Test not batched input
											a__					   =		image_processing(image_inputs[0]	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_		)
											self.assertEqual(
											    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

											# Test batched
											a__					   =		image_processing(a_	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_	,	batched=a_		)
											self.assertEqual(
											    encoded_images.shape	,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )	,	)




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

class        _UpperCamelCase	:


  """simple docstring"""





  snake_case_        =    'block_{0}.dat'



  def __init__( self					: int       ,       snake_case					: int      )	->			List[str]:


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     filename
         __magic_name__			:		Any        =     []



  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: int       ,       snake_case					: Any      )	->			List[Any]:


         '''simple docstring'''





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



  def 							_UpperCAmelCase      ( self					: str      )	->			List[str]:


         '''simple docstring'''





         return self.block_filenames



  def 							_UpperCAmelCase      ( self					: Union[str, Any]       ,       snake_case					: Dict       ,       snake_case					: int=None      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		List[str]        =     0
         with open(self.filename      ) as file:
                while True:
                       __magic_name__			:		Tuple        =     file.readlines(snake_case      )

                       if lines == []:
                              break

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

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




  def 							_UpperCAmelCase      ( self					: Tuple      )	->			Optional[int]:


         '''simple docstring'''





         map(os.remove       ,       self.block_filenames      )

class        _UpperCamelCase	:


  """simple docstring"""





  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: Dict      )	->			int:


         '''simple docstring'''





         __magic_name__			:		Any        =     -1
         __magic_name__			:		Union[str, Any]        =     None

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

         return min_index

class        _UpperCamelCase	:


  """simple docstring"""





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


         '''simple docstring'''





         __magic_name__			:		Dict        =     files
         __magic_name__			:		List[str]        =     set()
         __magic_name__			:		Any        =     len(snake_case      )
         __magic_name__			:		int        =     {i: None for i in range(self.num_buffers      )}



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


         '''simple docstring'''





         for i in range(self.num_buffers      ):
                if self.buffers[i] is None and i not in self.empty:
                       __magic_name__			:		int        =     self.files[i].readline()

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

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

         return True




  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: Optional[Any]      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__			:		List[str]        =     self.buffers[index]
         __magic_name__			:		Dict        =     None

         return value

class        _UpperCamelCase	:


  """simple docstring"""





  def __init__( self					: Any       ,       snake_case					: str      )	->			Dict:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     merge_strategy



  def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: int       ,       snake_case					: Dict       ,       snake_case					: Dict      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     FilesArray(self.get_file_handles(snake_case       ,       snake_case      )      )
         with open(snake_case       ,       '''w'''       ,       snake_case      ) as outfile:
                while buffers.refresh():
                       __magic_name__			:		List[str]        =     self.merge_strategy.select(buffers.get_dict()      )
                       outfile.write(buffers.unshift(snake_case      )      )




  def 							_UpperCAmelCase      ( self					: Union[str, Any]       ,       snake_case					: List[str]       ,       snake_case					: Optional[int]      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		str        =     {}

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

         return files

class        _UpperCamelCase	:


  """simple docstring"""





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


         '''simple docstring'''





         __magic_name__			:		int        =     block_size



  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: Optional[int]       ,       snake_case					: Dict=None      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__			:		Tuple        =     self.get_number_blocks(snake_case       ,       self.block_size      )
         __magic_name__			:		str        =     FileSplitter(snake_case      )
         splitter.split(self.block_size       ,       snake_case      )

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

         splitter.cleanup()




  def 							_UpperCAmelCase      ( self					: Tuple       ,       snake_case					: List[str]       ,       snake_case					: Union[str, Any]      )	->			Tuple:


         '''simple docstring'''





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







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



       """simple docstring"""







       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(lowerCamelCase   )







def       UpperCamelCase_					(     )						->     Optional[Any]:



       """simple docstring"""







       __magic_name__			:		Tuple        =     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'''   )
       __magic_name__			:		List[str]        =     parser.parse_args()

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


if __name__ == "__main__":
 main()
