

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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BlipImageProcessor





class 			__lowerCAmelCase					( unittest.TestCase							):




  def __init__(    self							,       lowerCAmelCase							,       lowerCAmelCase=7							,       lowerCAmelCase=3							,       lowerCAmelCase=18							,       lowerCAmelCase=30							,       lowerCAmelCase=400							,       lowerCAmelCase=True							,       lowerCAmelCase=None							,       lowerCAmelCase=True							,       lowerCAmelCase=False							,       lowerCAmelCase=[0.48145466, 0.4578275, 0.40821073]							,       lowerCAmelCase=[0.26862954, 0.26130258, 0.27577711]							,       lowerCAmelCase=True							,       )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=size if size is not None else {'height': 20, 'width': 20}
        _lowercase     	=parent
        _lowercase     	=batch_size
        _lowercase     	=num_channels
        _lowercase     	=image_size
        _lowercase     	=min_resolution
        _lowercase     	=max_resolution
        _lowercase     	=do_resize
        _lowercase     	=size
        _lowercase     	=do_normalize
        _lowercase     	=image_mean
        _lowercase     	=image_std
        _lowercase     	=do_pad
        _lowercase     	=do_convert_rgb




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }




  def   A__					(    self							,       lowerCAmelCase=False							,       lowerCAmelCase=False							,       lowerCAmelCase=False )  ->			Any:
        '''simple docstring'''






        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
              _lowercase     	=[]
              for i in range(self.batch_size ):
                    image_inputs.append(
                        np.random.randint(
                            255							,       size=(self.num_channels, self.max_resolution, self.max_resolution)							,       dtype=np.uinta ) )
        else:
              _lowercase     	=[]
              for i in range(self.batch_size ):
                    _lowercase	,	_lowercase     	=np.random.choice(np.arange(self.min_resolution							,       self.max_resolution )							,       2 )
                    image_inputs.append(np.random.randint(255							,       size=(self.num_channels, width, height)							,       dtype=np.uinta ) )

        if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              _lowercase     	=[Image.fromarray(np.moveaxis(lowerCAmelCase							,       0							,       -1 ) ) for x in image_inputs]

        if torchify:
              _lowercase     	=[torch.from_numpy(lowerCAmelCase ) for x in image_inputs]

        return image_inputs





@require_torch
@require_vision
class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE      ,      unittest.TestCase							):
  _a						      =  BlipImageProcessor if is_vision_available() else None




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=BlipImageProcessingTester(self )




  @property
  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=self.image_processing_class(**self.image_processor_dict )
        self.assertTrue(hasattr(lowerCAmelCase							,       'do_resize' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'size' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'do_normalize' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'image_mean' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'image_std' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'do_convert_rgb' ) )




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        pass




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=self.image_processing_class(**self.image_processor_dict )
        # create random PIL images
        _lowercase     	=self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,       Image.Image )

        # Test not batched input
        _lowercase     	=image_processor(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['height'],
                self.image_processor_tester.size['width'],
            )							,       )

        # Test batched
        _lowercase     	=image_processor(lowerCAmelCase							,       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['height'],
                self.image_processor_tester.size['width'],
            )							,       )




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.image_processing_class(**self.image_processor_dict )
        # create random numpy tensors
        _lowercase     	=self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase							,       numpify=lowerCAmelCase )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,       np.ndarray )

        # Test not batched input
        _lowercase     	=image_processor(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['height'],
                self.image_processor_tester.size['width'],
            )							,       )

        # Test batched
        _lowercase     	=image_processor(lowerCAmelCase							,       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['height'],
                self.image_processor_tester.size['width'],
            )							,       )




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=self.image_processing_class(**self.image_processor_dict )
        # create random PyTorch tensors
        _lowercase     	=self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase							,       torchify=lowerCAmelCase )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,       torch.Tensor )

        # Test not batched input
        _lowercase     	=image_processor(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['height'],
                self.image_processor_tester.size['width'],
            )							,       )

        # Test batched
        _lowercase     	=image_processor(lowerCAmelCase							,       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['height'],
                self.image_processor_tester.size['width'],
            )							,       )





@require_torch
@require_vision
class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE      ,      unittest.TestCase							):
  _a						      =  BlipImageProcessor if is_vision_available() else None




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=BlipImageProcessingTester(self							,       num_channels=4 )
        _lowercase     	=3




  @property
  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()




  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	=self.image_processing_class(**self.image_processor_dict )
        self.assertTrue(hasattr(lowerCAmelCase							,       'do_resize' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'size' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'do_normalize' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'image_mean' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'image_std' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'do_convert_rgb' ) )




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        pass




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=self.image_processing_class(**self.image_processor_dict )
        # create random PIL images
        _lowercase     	=self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,       Image.Image )

        # Test not batched input
        _lowercase     	=image_processor(image_inputs[0]							,       return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape							,       (
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )							,       )

        # Test batched
        _lowercase     	=image_processor(lowerCAmelCase							,       return_tensors='pt' ).pixel_values
        self.assertEqual(
            encoded_images.shape							,       (
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )							,       )





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





class 			__lowerCAmelCase					:
  _a						      =  """block_{0}.dat"""




  def __init__(    self							,       lowerCAmelCase )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	=filename
        _lowercase     	=[]




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.BLOCK_FILENAME_FORMAT.format(lowerCAmelCase )
        with open(lowerCAmelCase							,       'w' ) as file:
              file.write(lowerCAmelCase )
        self.block_filenames.append(lowerCAmelCase )




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        return self.block_filenames




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase=None )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=0
        with open(self.filename ) as file:
              while True:
                    _lowercase     	=file.readlines(lowerCAmelCase )

                    if lines == []:
                          break

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

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




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        map(os.remove							,       self.block_filenames )





class 			__lowerCAmelCase					:




  def   A__					(    self							,       lowerCAmelCase )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=-1
        _lowercase     	=None

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

        return min_index





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase )  ->			int:
        '''simple docstring'''






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




  def   A__					(    self )  ->			int:
        '''simple docstring'''






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




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






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

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

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

        return True




  def   A__					(    self							,       lowerCAmelCase )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=self.buffers[index]
        _lowercase     	=None

        return value





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase )  ->			Any:
        '''simple docstring'''






        _lowercase     	=merge_strategy




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			str:
        '''simple docstring'''






        _lowercase     	=FilesArray(self.get_file_handles(lowerCAmelCase							,       lowerCAmelCase ) )
        with open(lowerCAmelCase							,       'w'							,       lowerCAmelCase ) as outfile:
              while buffers.refresh():
                    _lowercase     	=self.merge_strategy.select(buffers.get_dict() )
                    outfile.write(buffers.unshift(lowerCAmelCase ) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	={}

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

        return files





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=block_size




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase=None )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.get_number_blocks(lowerCAmelCase							,       self.block_size )
        _lowercase     	=FileSplitter(lowerCAmelCase )
        splitter.split(self.block_size							,       lowerCAmelCase )

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

        splitter.cleanup()




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Tuple:
        '''simple docstring'''






        return (os.stat(lowerCAmelCase ).st_size / block_size) + 1
def 					a				(      A__     :       Tuple					)   ->				Optional[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(A__					)
def 					a				(      )   ->				str:





      """simple docstring"""



      _lowercase     	=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     	=parser.parse_args()

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


if __name__ == "__main__":
  main()


